# HG changeset patch # User Paulo Chiliguano # Date 1453574078 18000 # Node ID c268fcd7784890cad9f8a22216f665d9ca2b25da # Parent e8056343b36e54b2d44e8b98cffd7a6dde57ba0d paper IEEE diff -r e8056343b36e -r c268fcd77848 paper IEEE/IEEEbib.bst --- a/paper IEEE/IEEEbib.bst Sat Jan 23 13:22:55 2016 -0500 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,1021 +0,0 @@ -%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% IEEE.bst %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% -% Bibliography Syle file for articles according to IEEE instructions -% balemi@aut.ee.ethz.ch <22-JUN-93> -% modified from unsrt.bib. Contributions by Richard H. Roy - -ENTRY - { address - author - booktitle - chapter - edition - editor - howpublished - institution - journal - key - month - note - number - organization - pages - publisher - school - series - title - type - volume - year - } - {} - { label } - -INTEGERS { output.state before.all mid.sentence after.sentence after.block } - -FUNCTION {init.state.consts} -{ #0 'before.all := - #1 'mid.sentence := - #2 'after.sentence := - #3 'after.block := -} - -STRINGS { s t } - -FUNCTION {output.nonnull} -{ 's := - output.state mid.sentence = - { ", " * write$ } - { output.state after.block = -% next line commented out by rhr and changed to write comma -% { add.period$ write$ - { ", " * write$ - newline$ - "\newblock " write$ - } - { output.state before.all = - 'write$ - { add.period$ " " * write$ } - if$ - } - if$ - mid.sentence 'output.state := - } - if$ - s -} - -FUNCTION {output} -{ duplicate$ empty$ - 'pop$ - 'output.nonnull - if$ -} - -FUNCTION {output.check} -{ 't := - duplicate$ empty$ - { pop$ "empty " t * " in " * cite$ * warning$ } - 'output.nonnull - if$ -} - -FUNCTION {output.bibitem} -{ newline$ - "\bibitem{" write$ - cite$ write$ - "}" write$ - newline$ - "" - before.all 'output.state := -} - -FUNCTION {fin.entry} -{ add.period$ - write$ - newline$ -} - -% 5/24/89 rhr -% modified fin.entry function - prints note field after body of entry -%FUNCTION {fin.entry} -%{ add.period$ -% note empty$ -% 'write$ -% { "\par\bgroup\parindent=0em " * annote * "\par\egroup " * write$ -% } -% if$ -% newline$ -%} - -FUNCTION {new.block} -{ output.state before.all = - 'skip$ - { after.block 'output.state := } - if$ -} - -% new block without terminating last block with a comma -FUNCTION {new.ncblock} -{ - write$ - newline$ - "\newblock " - before.all 'output.state := -} - -FUNCTION {new.nccont} -{ - write$ - " " - before.all 'output.state := -} - -FUNCTION {new.sentence} -{ output.state after.block = - 'skip$ - { output.state before.all = - 'skip$ - { after.sentence 'output.state := } - if$ - } - if$ -} - -FUNCTION {not} -{ { #0 } - { #1 } - if$ -} - -FUNCTION {and} -{ 'skip$ - { pop$ #0 } - if$ -} - -FUNCTION {or} -{ { pop$ #1 } - 'skip$ - if$ -} - -FUNCTION {new.block.checka} -{ empty$ - 'skip$ - 'new.block - if$ -} - -FUNCTION {new.block.checkb} -{ empty$ - swap$ empty$ - and - 'skip$ - 'new.block - if$ -} - -FUNCTION {new.sentence.checka} -{ empty$ - 'skip$ - 'new.sentence - if$ -} - -FUNCTION {new.sentence.checkb} -{ empty$ - swap$ empty$ - and - 'skip$ - 'new.sentence - if$ -} - -FUNCTION {field.or.null} -{ duplicate$ empty$ - { pop$ "" } - 'skip$ - if$ -} - -FUNCTION {emphasize} -{ duplicate$ empty$ - { pop$ "" } - { "{\em " swap$ * "}" * } - if$ -} - -FUNCTION {boldface} -{ duplicate$ empty$ - { pop$ "" } - { "{\bf " swap$ * "}" * } - if$ -} - -%FUNCTION {boldface} -%{ 's swap$ := -% s "" = -% { "" } -% { "{\bf " s * "}" * } -% if$ -%} -% -INTEGERS { nameptr namesleft numnames } - -FUNCTION {format.names} -{ 's := - #1 'nameptr := - s num.names$ 'numnames := - numnames 'namesleft := - { namesleft #0 > } - { s nameptr "{ff~}{vv~}{ll}{, jj}" format.name$ 't := - nameptr #1 > - { namesleft #1 > - { ", " * t * } - { numnames #2 > - { "," * } - 'skip$ - if$ - t "others" = - { " et~al." * } - { " and " * t * } - if$ - } - if$ - } - 't - if$ - nameptr #1 + 'nameptr := - namesleft #1 - 'namesleft := - } - while$ -} - -FUNCTION {format.authors} -{ author empty$ - { "" } - { author format.names } - if$ -} - -FUNCTION {format.editors} -{ editor empty$ - { "" } - { editor format.names - editor num.names$ #1 > - { ", Eds." * } - { ", Ed." * } - if$ - } - if$ -} - -FUNCTION {format.title} -{ title empty$ - { "" } - { "``" title "t" change.case$ * } - if$ -} - -FUNCTION {n.dashify} -{ 't := - "" - { t empty$ not } - { t #1 #1 substring$ "-" = - { t #1 #2 substring$ "--" = not - { "--" * - t #2 global.max$ substring$ 't := - } - { { t #1 #1 substring$ "-" = } - { "-" * - t #2 global.max$ substring$ 't := - } - while$ - } - if$ - } - { t #1 #1 substring$ * - t #2 global.max$ substring$ 't := - } - if$ - } - while$ -} - -FUNCTION {format.date} -{ year empty$ - { month empty$ - { "" } - { "there's a month but no year in " cite$ * warning$ - month - } - if$ - } - { month empty$ - 'year - { month " " * year * } - if$ - } - if$ -} - -% FUNCTION {format.date} -% { year empty$ -% 'year -% { " " year * } -% if$ -% } - -FUNCTION {format.btitle} -{ title emphasize -} - -FUNCTION {tie.or.space.connect} -{ duplicate$ text.length$ #3 < - { "~" } - { " " } - if$ - swap$ * * -} - -FUNCTION {either.or.check} -{ empty$ - 'pop$ - { "can't use both " swap$ * " fields in " * cite$ * warning$ } - if$ -} - -FUNCTION {format.bvolume} -{ volume empty$ - { "" } - { "vol." volume tie.or.space.connect - series empty$ - 'skip$ - { " of " * series emphasize * } - if$ - "volume and number" number either.or.check - } - if$ -} - -FUNCTION {format.number.series} -{ volume empty$ - { number empty$ - { series field.or.null } - { output.state mid.sentence = - { "number" } - { "Number" } - if$ - number tie.or.space.connect - series empty$ - { "there's a number but no series in " cite$ * warning$ } - { " in " * series * } - if$ - } - if$ - } - { "" } - if$ -} - -FUNCTION {format.edition} -{ edition empty$ - { "" } - { output.state mid.sentence = - { edition "l" change.case$ " edition" * } - { edition "t" change.case$ " edition" * } - if$ - } - if$ -} - -INTEGERS { multiresult } - -FUNCTION {multi.page.check} -{ 't := - #0 'multiresult := - { multiresult not - t empty$ not - and - } - { t #1 #1 substring$ - duplicate$ "-" = - swap$ duplicate$ "," = - swap$ "+" = - or or - { #1 'multiresult := } - { t #2 global.max$ substring$ 't := } - if$ - } - while$ - multiresult -} - -FUNCTION {format.pages} -{ pages empty$ - { "" } - { pages multi.page.check - { "pp." pages n.dashify tie.or.space.connect } - { "p." pages tie.or.space.connect } - if$ - } - if$ -} - -FUNCTION {format.vol.num.pages} -{ -volume empty$ - {"" } - {"vol. " volume *} -if$ -number empty$ - 'skip$ - {", no. " number * *} -if$ -pages empty$ - 'skip$ - { duplicate$ empty$ - { pop$ format.pages } - { ", pp. " * pages n.dashify * } - if$ - } -if$ -} - -%FUNCTION {format.vol.num.pages} -%%boldface added 3/17/87 rhr -%{ volume field.or.null boldface -% number empty$ -% 'skip$ -% { "(" number * ")" * * -% volume empty$ -% { "there's a number but no volume in " cite$ * warning$ } -% 'skip$ -% if$ -% } -% if$ -% pages empty$ -% 'skip$ -% { duplicate$ empty$ -% { pop$ format.pages } -% { ":" * pages n.dashify * } -% if$ -% } -% if$ -%} - -FUNCTION {format.chapter.pages} -{ chapter empty$ - 'format.pages - { type empty$ - { "chapter" } - { type "l" change.case$ } - if$ - chapter tie.or.space.connect - pages empty$ - 'skip$ - { ", " * format.pages * } - if$ - } - if$ -} - -FUNCTION {format.in.ed.booktitle} -{ booktitle empty$ - { "" } - { editor empty$ - { "in " booktitle emphasize * } - { "in " booktitle emphasize * ", " * format.editors * } - if$ - } - if$ -} - -FUNCTION {empty.misc.check} -{ author empty$ title empty$ howpublished empty$ - month empty$ year empty$ note empty$ - and and and and and - { "all relevant fields are empty in " cite$ * warning$ } - 'skip$ - if$ -} - -FUNCTION {format.thesis.type} -{ type empty$ - 'skip$ - { pop$ - type "t" change.case$ - } - if$ -} - -FUNCTION {format.tr.number} -{ type empty$ - { "Tech. {R}ep." } - 'type - if$ - number empty$ - { "t" change.case$ } - { number tie.or.space.connect } - if$ -} - -FUNCTION {format.article.crossref} -{ key empty$ - { journal empty$ - { "need key or journal for " cite$ * " to crossref " * crossref * - warning$ - "" - } - { "In {\em " journal * "\/}" * } - if$ - } - { "In " key * } - if$ - " \cite{" * crossref * "}" * -} - -FUNCTION {format.crossref.editor} -{ editor #1 "{vv~}{ll}" format.name$ - editor num.names$ duplicate$ - #2 > - { pop$ " et~al." * } - { #2 < - 'skip$ - { editor #2 "{ff }{vv }{ll}{ jj}" format.name$ "others" = - { " et~al." * } - { " and " * editor #2 "{vv~}{ll}" format.name$ * } - if$ - } - if$ - } - if$ -} - -FUNCTION {format.book.crossref} -{ volume empty$ - { "empty volume in " cite$ * "'s crossref of " * crossref * warning$ - "In " - } - { "vol." volume tie.or.space.connect - " of " * - } - if$ - editor empty$ - editor field.or.null author field.or.null = - or - { key empty$ - { series empty$ - { "need editor, key, or series for " cite$ * " to crossref " * - crossref * warning$ - "" * - } - { "{\em " * series * "\/}" * } - if$ - } - { key * } - if$ - } - { format.crossref.editor * } - if$ - " \cite{" * crossref * "}" * -} - -FUNCTION {format.incoll.inproc.crossref} -{ editor empty$ - editor field.or.null author field.or.null = - or - { key empty$ - { booktitle empty$ - { "need editor, key, or booktitle for " cite$ * " to crossref " * - crossref * warning$ - "" - } - { "In {\em " booktitle * "\/}" * } - if$ - } - { "In " key * } - if$ - } - { "In " format.crossref.editor * } - if$ - " \cite{" * crossref * "}" * -} - -FUNCTION {article} -{ output.bibitem - format.authors "author" output.check - new.block - format.title ",''" * "title" output.check - new.ncblock - crossref missing$ - { journal emphasize "journal" output.check - format.vol.num.pages output - format.date "year" output.check - } - { format.article.crossref output.nonnull - format.pages output - } - if$ - new.block - note output - fin.entry -} - -FUNCTION {book} -{ output.bibitem - author empty$ - { format.editors "author and editor" output.check } - { format.authors output.nonnull - crossref missing$ - { "author and editor" editor either.or.check } - 'skip$ - if$ - } - if$ - new.block - format.btitle "title" output.check - crossref missing$ - { format.bvolume output - new.block - format.number.series output - new.sentence - publisher "publisher" output.check - address output - } - { new.block - format.book.crossref output.nonnull - } - if$ - format.edition output - format.date "year" output.check - new.block - note output - fin.entry -} - -FUNCTION {booklet} -{ output.bibitem - format.authors output - new.block - format.title ",''" * "title" output.check - new.nccont - howpublished address new.block.checkb - howpublished output - address output - format.date output - new.block - note output - fin.entry -} - -FUNCTION {inbook} -{ output.bibitem - author empty$ - { format.editors "author and editor" output.check } - { format.authors output.nonnull - crossref missing$ - { "author and editor" editor either.or.check } - 'skip$ - if$ - } - if$ - new.block - format.btitle "title" output.check - crossref missing$ - { format.bvolume output - format.chapter.pages "chapter and pages" output.check - new.block - format.number.series output - new.sentence - publisher "publisher" output.check - address output - } - { format.chapter.pages "chapter and pages" output.check - new.block - format.book.crossref output.nonnull - } - if$ - format.edition output - format.date "year" output.check - new.block - note output - fin.entry -} - -FUNCTION {incollection} -{ output.bibitem - format.authors "author" output.check - new.block - format.title ",''" * "title" output.check - new.ncblock - crossref missing$ - { format.in.ed.booktitle "booktitle" output.check - format.bvolume output - format.number.series output - format.chapter.pages output - new.sentence - publisher "publisher" output.check - address output - format.edition output - format.date "year" output.check - } - { format.incoll.inproc.crossref output.nonnull - format.chapter.pages output - } - if$ - new.block - note output - fin.entry -} - -FUNCTION {inproceedings} -{ output.bibitem - format.authors "author" output.check - new.block - format.title ",''" * "title" output.check - new.ncblock - crossref missing$ - { format.in.ed.booktitle "booktitle" output.check - address empty$ - { organization publisher new.sentence.checkb - organization output - format.date "year" output.check - } - { address output.nonnull - format.date "year" output.check - organization output - } - if$ - format.bvolume output - format.number.series output - format.pages output - publisher output - } - { format.incoll.inproc.crossref output.nonnull - format.pages output - } - if$ - new.block - note output - fin.entry -} - -FUNCTION {conference} { inproceedings } - -FUNCTION {manual} -{ output.bibitem - author empty$ - { organization empty$ - 'skip$ - { organization output.nonnull - address output - } - if$ - } - { format.authors output.nonnull } - if$ - new.block - format.btitle "title" output.check - author empty$ - { organization empty$ - { address new.block.checka - address output - } - 'skip$ - if$ - } - { organization address new.block.checkb - organization output - address output - } - if$ - format.edition output - format.date output - new.block - note output - fin.entry -} - -FUNCTION {mastersthesis} -{ output.bibitem - format.authors "author" output.check - new.block - format.title ",''" * "title" output.check - new.ncblock - "M.S. thesis" format.thesis.type output.nonnull - school "school" output.check - address output - format.date "year" output.check - new.block - note output - fin.entry -} - -FUNCTION {misc} -{ output.bibitem - format.authors output - title howpublished new.block.checkb - format.title ",''" * output - new.nccont - howpublished new.block.checka - howpublished output - format.date output - new.block - note output - fin.entry - empty.misc.check -} - -FUNCTION {phdthesis} -{ output.bibitem - format.authors "author" output.check - new.block - format.btitle "title" output.check - new.block - "Ph.D. thesis" format.thesis.type output.nonnull - school "school" output.check - address output - format.date "year" output.check - new.block - note output - fin.entry -} - -FUNCTION {proceedings} -{ output.bibitem - editor empty$ - { organization output } - { format.editors output.nonnull } - if$ - new.block - format.btitle "title" output.check - format.bvolume output - format.number.series output - address empty$ - { editor empty$ - { publisher new.sentence.checka } - { organization publisher new.sentence.checkb - organization output - } - if$ - publisher output - format.date "year" output.check - } - { address output.nonnull - format.date "year" output.check - new.sentence - editor empty$ - 'skip$ - { organization output } - if$ - publisher output - } - if$ - new.block - note output - fin.entry -} - -FUNCTION {techreport} -{ output.bibitem - format.authors "author" output.check - new.block - format.title ",''" * "title" output.check - new.ncblock - format.tr.number output.nonnull - institution "institution" output.check - address output - format.date "year" output.check - new.block - note output - fin.entry -} - -FUNCTION {unpublished} -{ output.bibitem - format.authors "author" output.check - new.block - format.title ",''" * "title" output.check - new.ncblock - note "note" output.check - format.date output - fin.entry -} - -FUNCTION {default.type} { misc } - -MACRO {jan} {"Jan."} - -MACRO {feb} {"Feb."} - -MACRO {mar} {"Mar."} - -MACRO {apr} {"Apr."} - -MACRO {may} {"May"} - -MACRO {jun} {"June"} - -MACRO {jul} {"July"} - -MACRO {aug} {"Aug."} - -MACRO {sep} {"Sept."} - -MACRO {oct} {"Oct."} - -MACRO {nov} {"Nov."} - -MACRO {dec} {"Dec."} - -MACRO {acmcs} {"ACM Computing Surveys"} - -MACRO {acta} {"Acta Informatica"} - -MACRO {cacm} {"Communications of the ACM"} - -MACRO {ibmjrd} {"IBM Journal of Research and Development"} - -MACRO {ibmsj} {"IBM Systems Journal"} - -MACRO {ieeese} {"IEEE Transactions on Software Engineering"} - -MACRO {ieeetc} {"IEEE Transactions on Computers"} - -MACRO {ieeetcad} - {"IEEE Transactions on Computer-Aided Design of Integrated Circuits"} - -MACRO {ipl} {"Information Processing Letters"} - -MACRO {jacm} {"Journal of the ACM"} - -MACRO {jcss} {"Journal of Computer and System Sciences"} - -MACRO {scp} {"Science of Computer Programming"} - -MACRO {sicomp} {"SIAM Journal on Computing"} - -MACRO {tocs} {"ACM Transactions on Computer Systems"} - -MACRO {tods} {"ACM Transactions on Database Systems"} - -MACRO {tog} {"ACM Transactions on Graphics"} - -MACRO {toms} {"ACM Transactions on Mathematical Software"} - -MACRO {toois} {"ACM Transactions on Office Information Systems"} - -MACRO {toplas} {"ACM Transactions on Programming Languages and Systems"} - -MACRO {tcs} {"Theoretical Computer Science"} - -READ - -STRINGS { longest.label } - -INTEGERS { number.label longest.label.width } - -FUNCTION {initialize.longest.label} -{ "" 'longest.label := - #1 'number.label := - #0 'longest.label.width := -} - -FUNCTION {longest.label.pass} -{ number.label int.to.str$ 'label := - number.label #1 + 'number.label := - label width$ longest.label.width > - { label 'longest.label := - label width$ 'longest.label.width := - } - 'skip$ - if$ -} - -EXECUTE {initialize.longest.label} - -ITERATE {longest.label.pass} - -FUNCTION {begin.bib} -{ preamble$ empty$ - 'skip$ - { preamble$ write$ newline$ } - if$ - "\begin{thebibliography}{" longest.label * "}" * write$ newline$ -} - -EXECUTE {begin.bib} - -EXECUTE {init.state.consts} - -ITERATE {call.type$} - -FUNCTION {end.bib} -{ newline$ - "\end{thebibliography}" write$ newline$ -} - -EXECUTE {end.bib} - -%%%%%%%%%%%%%%%%%%%%%%%%%%%%% End of IEEE.bst %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% diff -r e8056343b36e -r c268fcd77848 paper IEEE/Template.aux --- a/paper IEEE/Template.aux Sat Jan 23 13:22:55 2016 -0500 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,40 +0,0 @@ -\relax -\@writefile{toc}{\contentsline {section}{\numberline {1} Introduction}{1}} -\newlabel{sec:intro}{{1}{1}} -\@writefile{toc}{\contentsline {section}{\numberline {2} Formatting your paper}{1}} -\newlabel{sec:format}{{2}{1}} -\@writefile{toc}{\contentsline {section}{\numberline {3} PAGE TITLE SECTION}{1}} -\newlabel{sec:pagestyle}{{3}{1}} -\@writefile{toc}{\contentsline {section}{\numberline {4} TYPE-STYLE AND FONTS}{1}} -\newlabel{sec:typestyle}{{4}{1}} -\@writefile{toc}{\contentsline {section}{\numberline {5} MAJOR HEADINGS}{1}} -\newlabel{sec:majhead}{{5}{1}} -\@writefile{toc}{\contentsline {subsection}{\numberline {5.1} Subheadings}{1}} -\newlabel{ssec:subhead}{{5.1}{1}} -\@writefile{toc}{\contentsline {subsubsection}{\numberline {5.1.1} Sub-subheadings}{1}} -\newlabel{sssec:subsubhead}{{5.1.1}{1}} -\@writefile{toc}{\contentsline {section}{\numberline {6} PRINTING YOUR PAPER}{1}} -\newlabel{sec:print}{{6}{1}} -\citation{Lamp86} -\citation{Lamp86} -\citation{C2} -\@writefile{toc}{\contentsline {section}{\numberline {7} PAGE NUMBERING}{2}} -\newlabel{sec:page}{{7}{2}} -\@writefile{toc}{\contentsline {section}{\numberline {8} ILLUSTRATIONS, GRAPHS, AND PHOTOGRAPHS}{2}} -\newlabel{sec:illust}{{8}{2}} -\@writefile{toc}{\contentsline {section}{\numberline {9} FOOTNOTES}{2}} -\newlabel{sec:foot}{{9}{2}} -\@writefile{toc}{\contentsline {section}{\numberline {10} COPYRIGHT FORMS}{2}} -\newlabel{sec:copyright}{{10}{2}} -\@writefile{toc}{\contentsline {section}{\numberline {11} RELATION TO PRIOR WORK}{2}} -\newlabel{sec:prior}{{11}{2}} -\@writefile{lof}{\contentsline {figure}{\numberline {1}{\ignorespaces Example of placing a figure with experimental results.}}{2}} -\newlabel{fig:res}{{1}{2}} -\citation{C2} -\bibstyle{IEEEbib} -\bibdata{strings,refs} -\bibcite{Lamp86}{1} -\bibcite{C2}{2} -\@writefile{toc}{\contentsline {section}{\numberline {12} REFERENCES}{3}} -\newlabel{sec:refs}{{12}{3}} -\@writefile{toc}{\contentsline {section}{\numberline {13} References}{3}} diff -r e8056343b36e -r c268fcd77848 paper IEEE/Template.bbl --- a/paper IEEE/Template.bbl Sat Jan 23 13:22:55 2016 -0500 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,13 +0,0 @@ -\begin{thebibliography}{1} - -\bibitem{Lamp86} -A.B. Smith, C.D. Jones, and E.F. Roberts, -\newblock ``Article title,'' -\newblock {\em Journal}, vol. 62, pp. 291--294, January 1920. - -\bibitem{C2} -C.D. Jones, A.B. Smith, and E.F. Roberts, -\newblock ``Article title,'' -\newblock in {\em Proceedings Title}. IEEE, 2003, vol.~II, pp. 803--806. - -\end{thebibliography} diff -r e8056343b36e -r c268fcd77848 paper IEEE/Template.blg --- a/paper IEEE/Template.blg Sat Jan 23 13:22:55 2016 -0500 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,47 +0,0 @@ -This is BibTeX, Version 0.99d (TeX Live 2015) -Capacity: max_strings=35307, hash_size=35307, hash_prime=30011 -The top-level auxiliary file: Template.aux -The style file: IEEEbib.bst -Database file #1: strings.bib -Database file #2: refs.bib -You've used 2 entries, - 1838 wiz_defined-function locations, - 472 strings with 3692 characters, -and the built_in function-call counts, 647 in all, are: -= -- 60 -> -- 22 -< -- 2 -+ -- 8 -- -- 6 -* -- 52 -:= -- 97 -add.period$ -- 3 -call.type$ -- 2 -change.case$ -- 2 -chr.to.int$ -- 0 -cite$ -- 2 -duplicate$ -- 29 -empty$ -- 62 -format.name$ -- 6 -if$ -- 153 -int.to.chr$ -- 0 -int.to.str$ -- 2 -missing$ -- 2 -newline$ -- 13 -num.names$ -- 2 -pop$ -- 6 -preamble$ -- 1 -purify$ -- 0 -quote$ -- 0 -skip$ -- 17 -stack$ -- 0 -substring$ -- 49 -swap$ -- 13 -text.length$ -- 2 -text.prefix$ -- 0 -top$ -- 0 -type$ -- 0 -warning$ -- 0 -while$ -- 5 -width$ -- 3 -write$ -- 26 diff -r e8056343b36e -r c268fcd77848 paper IEEE/Template.log --- a/paper IEEE/Template.log Sat Jan 23 13:22:55 2016 -0500 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,287 +0,0 @@ -This is pdfTeX, Version 3.14159265-2.6-1.40.16 (TeX Live 2015) (preloaded format=pdflatex 2015.7.15) 14 SEP 2015 12:27 -entering extended mode - 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ICASSP/ICIP LaTeX style file, and -% IEEEbib.bst - IEEE bibliography style file. -% -------------------------------------------------------------------------- -\documentclass{article} -\usepackage{spconf,amsmath,graphicx} - -% Example definitions. -% -------------------- -\def\x{{\mathbf x}} -\def\L{{\cal L}} - -% Title. -% ------ -\title{AUTHOR GUIDELINES FOR ICASSP 2016 PROCEEDINGS MANUSCRIPTS} -% -% Single address. -% --------------- -\name{Author(s) Name(s)\thanks{Thanks to XYZ agency for funding.}} -\address{Author Affiliation(s)} -% -% For example: -% ------------ -%\address{School\\ -% Department\\ -% Address} -% -% Two addresses (uncomment and modify for two-address case). -% ---------------------------------------------------------- -%\twoauthors -% {A. Author-one, B. Author-two\sthanks{Thanks to XYZ agency for funding.}} -% {School A-B\\ -% Department A-B\\ -% Address A-B} -% {C. Author-three, D. Author-four\sthanks{The fourth author performed the work -% while at ...}} -% {School C-D\\ -% Department C-D\\ -% Address C-D} -% -\begin{document} -%\ninept -% -\maketitle -% -\begin{abstract} -The abstract should appear at the top of the left-hand column of text, about -0.5 inch (12 mm) below the title area and no more than 3.125 inches (80 mm) in -length. Leave a 0.5 inch (12 mm) space between the end of the abstract and the -beginning of the main text. The abstract should contain about 100 to 150 -words, and should be identical to the abstract text submitted electronically -along with the paper cover sheet. All manuscripts must be in English, printed -in black ink. -\end{abstract} -% -\begin{keywords} -One, two, three, four, five -\end{keywords} -% -\section{Introduction} -\label{sec:intro} - -These guidelines include complete descriptions of the fonts, spacing, and -related information for producing your proceedings manuscripts. Please follow -them and if you have any questions, direct them to Conference Management -Services, Inc.: Phone +1-979-846-6800 or email -to \\\texttt{papers@icassp2016.org}. - -\section{Formatting your paper} -\label{sec:format} - -All printed material, including text, illustrations, and charts, must be kept -within a print area of 7 inches (178 mm) wide by 9 inches (229 mm) high. Do -not write or print anything outside the print area. The top margin must be 1 -inch (25 mm), except for the title page, and the left margin must be 0.75 inch -(19 mm). All {\it text} must be in a two-column format. 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The work by -Smith and Cohen \cite{Lamp86} considers only fixed time-domain analysis and -the work by Jones et al \cite{C2} takes a different approach based on -fixed frequency partitioning. While the present study is related -to recent approaches in time-frequency analysis [3-5], it capitalizes -on a new feature space, which was not considered in these earlier -studies." - -\vfill\pagebreak - -\section{REFERENCES} -\label{sec:refs} - -List and number all bibliographical references at the end of the -paper. The references can be numbered in alphabetic order or in -order of appearance in the document. When referring to them in -the text, type the corresponding reference number in square -brackets as shown at the end of this sentence \cite{C2}. An -additional final page (the fifth page, in most cases) is -allowed, but must contain only references to the prior -literature. - -% References should be produced using the bibtex program from suitable -% BiBTeX files (here: strings, refs, manuals). The IEEEbib.bst bibliography -% style file from IEEE produces unsorted bibliography list. -% ------------------------------------------------------------------------- -\bibliographystyle{IEEEbib} -\bibliography{strings,refs} - -\end{document} diff -r e8056343b36e -r c268fcd77848 paper IEEE/chiliguano.aux --- a/paper IEEE/chiliguano.aux Sat Jan 23 13:22:55 2016 -0500 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,75 +0,0 @@ -\relax -\citation{melville2010recommender} -\citation{Yao2015453} -\citation{Burke2002331} -\citation{Hu2008263} -\citation{Yin2012896} -\citation{Dai20141760} -\citation{Lops2011} -\citation{Burke2002331} -\citation{Yoshii2008435} -\citation{NIPS2013_5004} -\citation{Bengio-et-al-2015-Book} -\citation{Bertin-Mahieux2011} -\@writefile{toc}{\contentsline {section}{\numberline {1} Introduction}{1}} -\newlabel{sec:intro}{{1}{1}} -\@writefile{toc}{\contentsline {section}{\numberline {2} Background}{1}} -\newlabel{sec:background}{{2}{1}} -\citation{pelikan2015estimation} -\citation{Ding2015451} -\citation{Santana:Bielza:Larrañaga:Lozano:Echegoyen:Mendiburu:Armañanzas:Shakya:2009:JSSOBK:v35i07} -\citation{Ding2015451} -\citation{Bertin-Mahieux2011} -\citation{1242} -\@writefile{toc}{\contentsline {section}{\numberline {3} METHODOLOGY}{2}} -\newlabel{sec:format}{{3}{2}} -\@writefile{lof}{\contentsline {figure}{\numberline {1}{\ignorespaces Flowchart of an EDA presented in\nobreakspace {}\cite {Ding2015451}}}{2}} -\newlabel{fig:eda}{{1}{2}} -\@writefile{lof}{\contentsline {figure}{\numberline {2}{\ignorespaces Diagram of hybrid music recommender}}{2}} -\newlabel{fig:hybrid}{{2}{2}} -\@writefile{toc}{\contentsline {subsection}{\numberline {3.1} Data collection}{2}} -\citation{NIPS2013_5004} -\citation{Tzanetakis2002293} -\citation{DBLP:journals/corr/KereliukSL15} -\citation{Liang2014781} -\citation{gallagher2007bayesian} -\citation{1242} -\@writefile{toc}{\contentsline {subsection}{\numberline {3.2} Probability of music genre representation}{3}} -\@writefile{toc}{\contentsline {subsection}{\numberline {3.3} User profile modeling with EDAs}{3}} -\newlabel{eq:fitness}{{1}{3}} -\@writefile{toc}{\contentsline {subsection}{\numberline {3.4} Top-N songs recommendation}{3}} -\@writefile{toc}{\contentsline {section}{\numberline {4} EXPERIMENTS}{3}} -\newlabel{sec:pagestyle}{{4}{3}} -\@writefile{toc}{\contentsline {subsection}{\numberline {4.1} Top-N evaluation}{3}} -\@writefile{toc}{\contentsline {section}{\numberline {5} RESULTS}{3}} -\newlabel{sec:typestyle}{{5}{3}} -\bibstyle{IEEEbib} -\bibdata{strings,refs} -\bibcite{melville2010recommender}{1} -\bibcite{Yao2015453}{2} -\bibcite{Burke2002331}{3} -\bibcite{Hu2008263}{4} -\bibcite{Yin2012896}{5} -\bibcite{Dai20141760}{6} -\bibcite{Lops2011}{7} -\bibcite{Yoshii2008435}{8} -\bibcite{NIPS2013_5004}{9} -\@writefile{lot}{\contentsline {table}{\numberline {1}{\ignorespaces Evaluation of recommender systems (N=5)}}{4}} -\newlabel{table:recn5}{{1}{4}} -\@writefile{lot}{\contentsline {table}{\numberline {2}{\ignorespaces Evaluation of recommender systems (N=10)}}{4}} -\newlabel{table:recn10}{{2}{4}} -\@writefile{lot}{\contentsline {table}{\numberline {3}{\ignorespaces Evaluation of recommender systems (N=20)}}{4}} -\newlabel{table:recn20}{{3}{4}} -\@writefile{toc}{\contentsline {section}{\numberline {6} CONCLUSIONS AND FURTHER WORK}{4}} -\newlabel{sec:refs}{{6}{4}} -\@writefile{toc}{\contentsline {section}{\numberline {7} References}{4}} -\bibcite{Bengio-et-al-2015-Book}{10} -\bibcite{Bertin-Mahieux2011}{11} -\bibcite{pelikan2015estimation}{12} -\bibcite{Ding2015451}{13} -\bibcite{Santana:Bielza:Larrañaga:Lozano:Echegoyen:Mendiburu:Armañanzas:Shakya:2009:JSSOBK:v35i07}{14} -\bibcite{1242}{15} -\bibcite{Tzanetakis2002293}{16} -\bibcite{DBLP:journals/corr/KereliukSL15}{17} -\bibcite{Liang2014781}{18} -\bibcite{gallagher2007bayesian}{19} diff -r e8056343b36e -r c268fcd77848 paper IEEE/chiliguano.bbl --- a/paper IEEE/chiliguano.bbl Sat Jan 23 13:22:55 2016 -0500 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,121 +0,0 @@ -\begin{thebibliography}{10} - -\bibitem{melville2010recommender} -Prem Melville and Vikas Sindhwani, -\newblock ``Recommender systems,'' -\newblock in {\em Encyclopedia of machine learning}, pp. 829--838. Springer, - 2010. - -\bibitem{Yao2015453} -L.~Yao, Q.Z. Sheng, A.H.H. Ngu, J.~Yu, and A.~Segev, -\newblock ``Unified collaborative and content-based web service - recommendation,'' -\newblock {\em IEEE Transactions on Services Computing}, vol. 8, no. 3, pp. - 453--466, 2015. - -\bibitem{Burke2002331} -R.~Burke, -\newblock ``Hybrid recommender systems: Survey and experiments,'' -\newblock {\em User Modelling and User-Adapted Interaction}, vol. 12, no. 4, - pp. 331--370, 2002. - -\bibitem{Hu2008263} -Y.~Hu, C.~Volinsky, and Y.~Koren, -\newblock ``Collaborative filtering for implicit feedback datasets,'' -\newblock {\em Proceedings - IEEE International Conference on Data Mining, - ICDM}, pp. 263--272, 2008. - -\bibitem{Yin2012896} -H.~Yin, B.~Cui, J.~Li, J.~Yao, and C.~Chen, -\newblock ``Challenging the long tail recommendation,'' -\newblock {\em Proceedings of the VLDB Endowment}, vol. 5, no. 9, pp. 896--907, - 2012. - -\bibitem{Dai20141760} -C.~Dai, F.~Qian, W.~Jiang, Z.~Wang, and Z.~Wu, -\newblock ``A personalized recommendation system for netease dating site,'' -\newblock {\em Proceedings of the VLDB Endowment}, vol. 7, no. 13, pp. - 1760--1765, 2014. - -\bibitem{Lops2011} -Pasquale Lops, Marco de~Gemmis, and Giovanni Semeraro, -\newblock ``Content-based recommender systems: State of the art and trends,'' -\newblock in {\em Recommender Systems Handbook}, Francesco Ricci, Lior Rokach, - Bracha Shapira, and Paul~B. Kantor, Eds., pp. 73--105. Springer US, 2011. - -\bibitem{Yoshii2008435} -K.~Yoshii, M.~Goto, K.~Komatani, T.~Ogata, and H.G. Okuno, -\newblock ``An efficient hybrid music recommender system using an incrementally - trainable probabilistic generative model,'' -\newblock {\em IEEE Transactions on Audio, Speech and Language Processing}, - vol. 16, no. 2, pp. 435--447, 2008. - -\bibitem{NIPS2013_5004} -Aaron van~den Oord, Sander Dieleman, and Benjamin Schrauwen, -\newblock ``Deep content-based music recommendation,'' -\newblock in {\em Advances in Neural Information Processing Systems 26}, C.J.C. - Burges, L.~Bottou, M.~Welling, Z.~Ghahramani, and K.Q. Weinberger, Eds., pp. - 2643--2651. Curran Associates, Inc., 2013. - -\bibitem{Bengio-et-al-2015-Book} -Yoshua Bengio, Ian~J. Goodfellow, and Aaron Courville, -\newblock ``Deep learning,'' -\newblock Book in preparation for MIT Press, 2015. - -\bibitem{Bertin-Mahieux2011} -Thierry Bertin-Mahieux, Daniel~P.W. Ellis, Brian Whitman, and Paul Lamere, -\newblock ``The million song dataset,'' -\newblock in {\em {Proceedings of the 12th International Conference on Music - Information Retrieval ({ISMIR} 2011)}}, 2011. - -\bibitem{pelikan2015estimation} -Martin Pelikan, Mark~W Hauschild, and Fernando~G Lobo, -\newblock ``Estimation of distribution algorithms,'' -\newblock in {\em Springer Handbook of Computational Intelligence}, pp. - 899--928. Springer, 2015. - -\bibitem{Ding2015451} -C.~Ding, L.~Ding, and W.~Peng, -\newblock ``Comparison of effects of different learning methods on estimation - of distribution algorithms,'' -\newblock {\em Journal of Software Engineering}, vol. 9, no. 3, pp. 451--468, - 2015. - -\bibitem{Santana:Bielza:Larrañaga:Lozano:Echegoyen:Mendiburu:Armañanzas:Shakya:2009:JSSOBK:v35i07} -Roberto Santana, Concha Bielza, Pedro Larrañaga, Jose~A. Lozano, Carlos - Echegoyen, Alexander Mendiburu, Rubén Armañanzas, and Siddartha Shakya, -\newblock ``Mateda-2.0: A matlab package for the implementation and analysis of - estimation of distribution algorithms,'' -\newblock {\em Journal of Statistical Software}, vol. 35, no. 7, pp. 1--30, 7 - 2010. - -\bibitem{1242} -\`{O}. Celma, -\newblock {\em Music Recommendation and Discovery in the Long Tail}, -\newblock Ph.D. thesis, Universitat Pompeu Fabra, Barcelona, 2008. - -\bibitem{Tzanetakis2002293} -G.~Tzanetakis and P.~Cook, -\newblock ``Musical genre classification of audio signals,'' -\newblock {\em IEEE Transactions on Speech and Audio Processing}, vol. 10, no. - 5, pp. 293--302, 2002. - -\bibitem{DBLP:journals/corr/KereliukSL15} -Corey Kereliuk, Bob~L. Sturm, and Jan Larsen, -\newblock ``Deep learning and music adversaries,'' -\newblock {\em CoRR}, vol. abs/1507.04761, 2015. - -\bibitem{Liang2014781} -T.~Liang, Y.~Liang, J.~Fan, and J.~Zhao, -\newblock ``A hybrid recommendation model based on estimation of distribution - algorithms,'' -\newblock {\em Journal of Computational Information Systems}, vol. 10, no. 2, - pp. 781--788, 2014. - -\bibitem{gallagher2007bayesian} -Marcus Gallagher, Ian Wood, Jonathan Keith, and George Sofronov, -\newblock ``Bayesian inference in estimation of distribution algorithms,'' -\newblock in {\em Evolutionary Computation, 2007. CEC 2007. IEEE Congress on}. - IEEE, 2007, pp. 127--133. - -\end{thebibliography} diff -r e8056343b36e -r c268fcd77848 paper IEEE/chiliguano.blg --- a/paper IEEE/chiliguano.blg Sat Jan 23 13:22:55 2016 -0500 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,47 +0,0 @@ -This is BibTeX, Version 0.99d (TeX Live 2015) -Capacity: max_strings=35307, hash_size=35307, hash_prime=30011 -The top-level auxiliary file: chiliguano.aux -The style file: IEEEbib.bst -Database file #1: strings.bib -Database file #2: refs.bib -You've used 19 entries, - 1838 wiz_defined-function locations, - 587 strings with 7388 characters, -and the built_in function-call counts, 5356 in all, are: -= -- 477 -> -- 268 -< -- 6 -+ -- 95 -- -- 76 -* -- 445 -:= -- 842 -add.period$ -- 24 -call.type$ -- 19 -change.case$ -- 18 -chr.to.int$ -- 0 -cite$ -- 19 -duplicate$ -- 228 -empty$ -- 487 -format.name$ -- 76 -if$ -- 1224 -int.to.chr$ -- 0 -int.to.str$ -- 19 -missing$ -- 17 -newline$ -- 98 -num.names$ -- 23 -pop$ -- 50 -preamble$ -- 1 -purify$ -- 0 -quote$ -- 0 -skip$ -- 104 -stack$ -- 0 -substring$ -- 377 -swap$ -- 74 -text.length$ -- 6 -text.prefix$ -- 0 -top$ -- 0 -type$ -- 0 -warning$ -- 0 -while$ -- 47 -width$ -- 21 -write$ -- 215 diff -r e8056343b36e -r c268fcd77848 paper IEEE/chiliguano.log --- a/paper IEEE/chiliguano.log Sat Jan 23 13:22:55 2016 -0500 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,296 +0,0 @@ -This is pdfTeX, Version 3.14159265-2.6-1.40.16 (TeX Live 2015) (preloaded format=pdflatex 2015.7.15) 23 JAN 2016 13:19 -entering extended mode - 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Sturm, and Jan Larsen, - [] - - -Underfull \hbox (badness 1189) in paragraph at lines 116--120 -[]\OT1/ptm/m/n/10 Marcus Gal-lagher, Ian Wood, Jonathan Keith, and - [] - -) [5 - -] (./chiliguano.aux) ) -Here is how much of TeX's memory you used: - 2207 strings out of 493089 - 29903 string characters out of 6134842 - 101195 words of memory out of 5000000 - 5664 multiletter control sequences out of 15000+600000 - 18923 words of font info for 42 fonts, out of 8000000 for 9000 - 1141 hyphenation exceptions out of 8191 - 37i,12n,23p,2287b,281s stack positions out of 5000i,500n,10000p,200000b,80000s -{/usr/local/texlive/2015/texmf-dist/fonts/enc/dvips/base/ -8r.enc}< -/usr/local/texlive/2015/texmf-dist/fonts/type1/public/amsfonts/cm/cmsy10.pfb> -Output written on chiliguano.pdf (5 pages, 218570 bytes). -PDF statistics: - 70 PDF objects out of 1000 (max. 8388607) - 47 compressed objects within 1 object stream - 0 named destinations out of 1000 (max. 500000) - 11 words of extra memory for PDF output out of 10000 (max. 10000000) - diff -r e8056343b36e -r c268fcd77848 paper IEEE/chiliguano.pdf Binary file paper IEEE/chiliguano.pdf has changed diff -r e8056343b36e -r c268fcd77848 paper IEEE/chiliguano.synctex.gz Binary file paper IEEE/chiliguano.synctex.gz has changed diff -r e8056343b36e -r c268fcd77848 paper IEEE/chiliguano.tex --- a/paper IEEE/chiliguano.tex Sat Jan 23 13:22:55 2016 -0500 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,289 +0,0 @@ -% Template for ICASSP-2016 paper; to be used with: -% spconf.sty - ICASSP/ICIP LaTeX style file, and -% IEEEbib.bst - IEEE bibliography style file. -% -------------------------------------------------------------------------- -\documentclass{article} -\usepackage{spconf,amsmath,graphicx} - -% Example definitions. -% -------------------- -\def\x{{\mathbf x}} -\def\L{{\cal L}} - -% Title. -% ------ -\title{HYBRID MUSIC RECOMMENDER USING CONTENT-BASED AND SOCIAL INFORMATION} -% -% Single address. -% --------------- -\name{Paulo Chiliguano, Gyorgy Fazekas}%\thanks{Thanks to XYZ agency for funding.}} -\address{Queen Mary, University of London\\School of Electronic Engineering and Computer Science\\Mile End Road, London E1 4NS, UK} -% -% For example: -% ------------ -%\address{School\\ -% Department\\ -% Address} -% -% Two addresses (uncomment and modify for two-address case). -% ---------------------------------------------------------- -%\twoauthors -% {A. Author-one, B. Author-two\sthanks{Thanks to XYZ agency for funding.}} -% {School A-B\\ -% Department A-B\\ -% Address A-B} -% {C. Author-three, D. Author-four\sthanks{The fourth author performed the work -% while at ...}} -% {School C-D\\ -% Department C-D\\ -% Address C-D} -% -\begin{document} -%\ninept -% -\maketitle -% -\begin{abstract} -Internet resources available today, including songs, albums, playlists or podcasts, that a user cannot discover require a tool for filtering the items that a user might consider relevant. Several recommendation techniques have been developed since the Internet explosion to achieve this filtering task. In an attempt to recommend relevant songs to users, we propose an hybrid music recommender system that considers real-world users information and high-level representation for audio data. The system uses a convolutional deep neural network to represent audio segments by genre and uses estimation of distribution algorithms to represent the user listening behavior. Finally, content-based filtering is employed to generate Top-N recommendations. Our proposed hybrid music recommender presents a convincing performance improvement over the content-based baseline model. -\end{abstract} -% -\begin{keywords} - Deep Learning, Convolutional Neural Networks, Estimation of Distribution Algorithms, Recommender Systems, MIR. - -\end{keywords} -% -\section{Introduction} -\label{sec:intro} - -Recommender systems~\cite{melville2010recommender} are software or technical facilities to provide items suggestions or predict customer preferences by using prior user information. These systems play an important role in commercial applications to increase sales and improve user satisfaction. In general, recommender systems can be categorized in two major groups: collaborative filtering (CF) and content-based (CB) filtering. In CF~\cite{Yao2015453, Burke2002331}, a \emph{m$\times$n} rating matrix represents the relationships between \textit{m} users and \textit{n} items and recommendations are based on the computed similarities between rows (for users) or columns (for items). CF can be further subdivided in neighborhood models~\cite{Hu2008263}. The recommendation process in CF is independent of the item features. However, CF ignores the items in the \textit{long tail}~\cite{Yin2012896} and it is unable to handle the \textit{cold-start}~\cite{Dai20141760} problem. On the other hand, CB filtering~\cite{Lops2011} is based on the analysis of the features that describe the items. The recommendation component consists in matching up the attributes of the items that a user has already rated, usually referred as the \textit{user profile}, against the attributes of a previously unseen products to produce a list of \emph{top-N} recommendations. The recommendation process in CB is entirely based on the attributes of the items, thus, the recommendations produced for each user is independent from the other users. Also, a CB recommender allows to recommend items that do not have any rating, therefore, the effects of cold-start problem can be diminished. However, the quality of recommendations from CB techniques depends on the size of historical dataset. A hybrid recommender~\cite{Burke2002331} system is usually developed through the combination of CF and CB recommendation techniques to boost the advantages of CF by considering the feedback (rating) of users and the advantages of CB by taking into count the item attributes. Methods of combining recommendation techniques to accomplish hybridization can be classified in weighted, switching, mixed, feature combination, cascade, feature augmentation and meta-level methods. - -In Section~\ref{sec:background}, we revise related work in music recommender systems. In Section~\ref{sec:format}, we present our proposed hybrid recommendation approach and its stages, also we describe the music genre representation of audio and user profile modeling. In Section~\ref{sec:pagestyle}, we present the experiments and evaluation protocols to assess the performance of the hybrid recommender. In Section~\ref{sec:typestyle}, we discuss and analyze the results from the conducted experiments to evaluate the proposed hybrid music recommender. We conclude in Section~\ref{sec:refs}, we present the conclusions and some thoughts for further research. - - -\section{Background} -\label{sec:background} - -A hybrid music recommender system proposed by~\cite{Yoshii2008435} considered the rating scores collected from Amazon.co.jp and acoustic features derived from the signals of musical pieces corresponding to Japanese CD singles that were ranked in weekly top-20 from April 2000 to December 2005. Acoustic features for each piece are represented as a \textit{bag-of-timbres}, i.e., a set of weights of polyphonic timbres, equivalent to a 13-dimensional MFCC representation. Bags of timbres are computed with a Gaussian Mixture Model, considering the same combination of Gaussians for every musical piece. A music recommender proposed by~\cite{NIPS2013_5004} use a latent factor model for CB recommendation and the implementation of a convolutional neural network (CNN)~\cite{Bengio-et-al-2015-Book} to predict the latent factors from music audio. To obtain 50-dimension latent vectors, they used a weighted matrix factorization (WMF) algorithm on the Taste Profile Subset~\cite{Bertin-Mahieux2011}. Also, they retrieved audio clips for over 99\% of the songs in the dataset from 7digital.com. To train the CNN, the latent vectors obtained through the WMF are used as ground truth. The input of the CNN is a log-compressed mel-spectrogram with 128 components computed from windows of 1,024 samples and a hop size of 512 samples (sampling rate of 22,050 Hz) of a 3 seconds window sampled randomly for each audio clip. They used 10-fold cross validation and obtained an average area under the ROC curve (AUC) of 0.86703 for prediction based on the latent factor vectors, outperforming the bag-of-timbres approach. - -Inspired by natural selection of species, estimation of distribution algorithms (EDAs)~\cite{pelikan2015estimation, Ding2015451, Santana:Bielza:Larrañaga:Lozano:Echegoyen:Mendiburu:Armañanzas:Shakya:2009:JSSOBK:v35i07} are optimization techniques that estimate a probabilistic model from a sample of promising individuals. The sample is used to generate a new population that satisfy a criteria, i.e., minimization of a \textit{fitness} function. In Figure~\ref{fig:eda}, we show the general flowchart of an EDA. Regarding to the types of distributions that an EDA are able to capture can be categorized in four broad groups: discrete variables EDA, permutation EDA, real-valued vectors EDA and genetic programming EDA. - -In this paper, we present a hybrid music recommender to mitigate the cold-start problem in recommendation strategies. First, we use CNNs to describe the time-frequency content of an audio clip with a n-dimensional vector, whose dimensions represent the probability of a clip to belong to an specific music genre. Second, as a primary contribution, EDAs are investigated to model user profiles in terms of probabilities of music genres preferences. The algorithms use play count and the content vector of each song in the user's collection to optimize the profile. To our knowledge, this is the first approach that uses a continuous EDA for user profile modeling in recommender systems. - -\begin{figure}[htb] - - \begin{minipage}[b]{1.0\linewidth} - \centering - \centerline{\includegraphics[width=8.5cm]{eda.png}} - % \vspace{2.0cm} - %\centerline{(a) Result 1}\medskip - \end{minipage} - % - \caption{Flowchart of an EDA presented in~\cite{Ding2015451}} - \label{fig:eda} - % -\end{figure} - - - -\section{METHODOLOGY} -\label{sec:format} - -The methodology used to develop our hybrid music recommender, shown in Figure~\ref{fig:hybrid} consists of four main stages. First, the collection of real world user-item data corresponding to the play counts of specific songs and the fetching of audio clips of the unique identified songs in the dataset. Secondly, the implementation of the CNN to represent the audio clips in terms of music genre probabilities as n-dimensional vectors. Next, permutation EDA and a continuous EDA are investigated to model user profiles based on the rated songs above a threshold. Finally, the process of top-N recommendation for the baseline and the hybrid recommender is described. - -\begin{figure}[htb] - - \begin{minipage}[b]{1.0\linewidth} - \centering - \centerline{\includegraphics[width=8.5cm]{hybrid.png}} - % \vspace{2.0cm} - %\centerline{(a) Result 1}\medskip - \end{minipage} - % - \caption{Diagram of hybrid music recommender} - \label{fig:hybrid} - % -\end{figure} - -\subsection{Data collection} -The Million Song Dataset~\cite{Bertin-Mahieux2011} is a collection of audio features and metadata for a million contemporary popular music tracks which provides ground truth for evaluation research in Music Information Retrieval (MIR). This collection is also complemented by the Taste Profile subset which provides 48,373,586 triplets, each of them consist of anonymous user ID, Echo Nest song ID and play count. We choose this dataset because it is publicly available and it contains enough data for user modeling and recommender evaluation. However, it has potential mismatches between song ID and metadata, thus, we filter the triplets without errors in song IDs, obtaining 45,795,100 triplets. In addition, we reduce significantly the size of the dataset for experimental purposes and we consider only the users with more than 1,000 played songs and select the song identifiers of 1,500 most played songs. At the end, we obtain 65,327 triplets. For each song ID of the 1,500 most played songs, we retrieve the Echo Nest track ID and preview URL to fetch audio data from 7digital, resulting in a total of 640 available MP3 clips with a duration of 30 to 60 seconds (44.1 kHz, 128 kbps, stereo). - -The reduced taste profile dataset represent the user listening habits as implicit feedback, i.e., play counts of songs, it is necessary to normalize the listening habits as explicit feedback, i.e., range of values $[1\ldots5]$ that indicate how much a user likes a song. Normalization of play counts is computed with the complementary cumulative distribution of play counts of a user, following the procedure given by~\cite{1242}. - -\subsection{Probability of music genre representation} - -Following the steps of~\cite{NIPS2013_5004}, a segment equivalent to 3 seconds of each audio clip is loaded at a sampling rate of 22,050 Hz and converted to mono channel. For every segment, a mel-scaled power spectrogram with 128 bands is computed from windows of 1,024 samples with a hop size of 512 samples, resulting in a spectrogram of 130 frames with 128 components. Next, the spectrogram is converted to logarithmic scale in dB using peak power as reference. The spectrograms are normalized to have zero mean and unit variance in each frequency band. We use the GTZAN dataset~\cite{Tzanetakis2002293} as ground truth for training, validation and testing the CNN. - -In our project, we recreate a similar CNN architecture for music genre classification in~\cite{DBLP:journals/corr/KereliukSL15}. A batch size of 20 and a dropout rate of 0.20 for the convolutional layer units are considered. The reshape of the spectrograms (130 frames$\times$128 frequency bands) to a 4-dimension tensor, compatible with the input of the first convolutional layer (batch size$\times$1$\times$130$\times$128), is required. The first convolutional layer consists of 32 filters, each one with a size of 8 frames, with a max-pooling downsampling of 4, to reduce the size of the spectrogram along the time axis. The size of the resulting spectrogram is 30$\times$128 and the output of this first convolutional layer is a 4-dimension tensor with a size of 20$\times$32$\times$30$\times$128. The second convolutional layer consists of 32 filters, each one with a size of 8 frames, with a max-pooling downsampling of 4, to reduce the size of the spectrogram obtained in the first layer. The size of the new spectrogram is 5$\times$128 and the output of this second convolutional layer is a 4-dimension tensor with a size of 20$\times$32$\times$5$\times$128. Following the convolution process, the reshape of the 4-dimensional tensor of the output of the second convolutional layer is required to feed the fully connected multi-layer perceptron (MLP). The MLP consists of 500 ReLUs. Finally, the classification of music genre is accomplished with a logistic regression layer with 10 softmax units. Each output value corresponds to a music genre and each value represents the probability of a song to belong to a specific music genre. - -%n the particular case for the time-frequency representation of each audio clip in the GTZAN dataset, we generate a list of the genre associated to each audio fragment that represent the target values (ground truth). This procedure for the GTZAN dataset is repeated for 9 times, considering the rest of 3-seconds fragments in each audio clip of the dataset for training, validation and testing of the CDNN - -%In our approach, we describe the songs in terms of n-dimensional vectors. Each dimension in the vector represent the probability of the song to belong to a music genre. The probability estimation is obtained from a music classifier implemented with a deep learning technique. - -\subsection{User profile modeling with EDAs} - -In our project, we model each user profile following the EDA approach of~\cite{Liang2014781} by minimizing a fitness function given by Equation~\eqref{eq:fitness} -\ -\begin{equation} -fitness(profile_u) =\sum_{i\in S_u}\log(r_{u,i}\times sim(profile_u,t_i)) -\label{eq:fitness} -\end{equation} -where \emph{$sim(profile_u,t_i)$} is the cosine similarity between the profile of user \emph{u} and the vector of probabilities of the song \emph{i}, which is in the subset of relevant songs \emph{$S_u$}, and \emph{$r_{u,i}$} is the rating of song \emph{i} given by user \emph{u}. In this approach, the probability values are discretized in 50 evenly spaced values over the interval $[0.1, 0.9]$. - -Moreover, we extended the EDA approach using the continuous univariate marginal distribution algorithm $UMDA_c^G$ of~\cite{gallagher2007bayesian}. - -\subsection{Top-N songs recommendation} - -In our hybrid music model, the CB filtering module computes the similarity between a user interest profile and a each song vector in the test set. The songs are ranked in descending order and the first $N$ songs of this list are recommended. - -\section{EXPERIMENTS} -\label{sec:pagestyle} -The hybrid music recommender system proposed in this project is evaluated through an off-line experiment and the results are presented with decision based metrics: Precision, recall, F1 measure and accuracy~\cite{1242}. - -The reduced taste profile dataset is split in a training and a test set. For each user in the dataset, a random sample corresponding to 20 \% of the total number of ratings is assigned to the test set, and the rest 80 \% is assigned to the training set. The split process is iterated for 10 times, resulting in a total of 10 training and 10 test sets. - -\subsection{Top-N evaluation} -For each song in the user test set, we look up if the song is included or not in the top-N recommendations. If the song rating is above the threshold (we find a rating $\geq$ 3 out of 5 suitable) and the song is ranked in the top-N recommendations, we count as a true positive, otherwise, if the song is not included in the recommendations we count as false negative. On the other hand, if the song rating is below the threshold and the song appears in the top-N recommendations, we count as a false positive, otherwise, if the song is not included on the recommendations, we count as a false negative. - -%The paper title (on the first page) should begin 1.38 inches (35 mm) from the -%top edge of the page, centered, completely capitalized, and in Times 14-point, -%boldface type. The authors' name(s) and affiliation(s) appear below the title -%in capital and lower case letters. Papers with multiple authors and -%affiliations may require two or more lines for this information. Please note -%that papers should not be submitted blind; include the authors' names on the -%PDF. - -\section{RESULTS} -\label{sec:typestyle} -In general, the results of the top-N evaluation demonstrate that the hybrid music recommender based on a permutation EDA presents a better performance compared with both the CB recommender and the hybrid approach based on a continuous EDA. In Table~\ref{table:recn5}, the results of top-5 recommendation are shown. -\begin{table}[htb] - \resizebox{0.6\linewidth}{!}{\centering % used for centering table - \begin{minipage}[b]{1.0\linewidth} - \begin{tabular}{c c c c c} % centered columns (4 columns) - \hline\hline %inserts double horizontal lines - Recommender & Precision & Recall & F1 & Accuracy \\ [0.5ex] % inserts table - %heading - \hline % inserts single horizontal line - Content-based (baseline) & 0.275 $\pm$ 0.087 & 0.010 $\pm$ 0.003 & 0.020 $\pm$ 0.007 & 0.681 $\pm$ 0.008 \\ % inserting body of the table - Hybrid (permutation EDA) & \textbf{0.391 $\pm$ 0.182} & \textbf{0.013 $\pm$ 0.007} & \textbf{0.025 $\pm$ 0.013} & \textbf{0.685 $\pm$ 0.009} \\ - Hybrid (continuous UMDA) & 0.318 $\pm$ 0.142 & 0.011 $\pm$ 0.005 & 0.021 $\pm$ 0.011 & 0.683 $\pm$ 0.009 \\ [1ex] % [1ex] adds vertical space - \hline %inserts single line - \end{tabular} - \end{minipage}} - - \caption{Evaluation of recommender systems (N=5)} % title of Table - \label{table:recn5} % is used to refer this table in the text -\end{table} - -In Table~\ref{table:recn10}, the results of top-10 songs recommendation are shown. In this case, the precision value improves for the CB recommender. The accuracy values for all recommender systems tend to decrease. -\begin{table}[htb] - \resizebox{0.6\linewidth}{!}{\centering % used for centering table - \begin{minipage}[b]{1.0\linewidth} - \begin{tabular}{c c c c c} % centered columns (4 columns) - \hline\hline %inserts double horizontal lines - Recommender & Precision & Recall & F1 & Accuracy \\ [0.5ex] % inserts table - %heading - \hline % inserts single horizontal line - Content-based (baseline) & 0.301 $\pm$ 0.059 & 0.022 $\pm$ 0.007 & 0.041 $\pm$ 0.012 & 0.678 $\pm$ 0.007 \\ % inserting body of the table - Hybrid (permutation EDA) & \textbf{0.370 $\pm$ 0.073} & \textbf{0.024 $\pm$ 0.007} & \textbf{0.045 $\pm$ 0.013} & \textbf{0.682 $\pm$ 0.009} \\ - Hybrid (continuous UMDA) & 0.309 $\pm$ 0.100 & 0.019 $\pm$ 0.007 & 0.036 $\pm$ 0.013 & 0.679 $\pm$ 0.009 \\ [1ex] % [1ex] adds vertical space - \hline %inserts single line - \end{tabular} - \end{minipage}} - - \caption{Evaluation of recommender systems (N=10)} % title of Table - \label{table:recn10} % is used to refer this table in the text -\end{table} - -In Table~\ref{table:recn20}, the results of top-20 songs recommendation are shown. In this case, the recall values rise for all the recommender systems, compared with the top-5 and top-10 recommendations, but the precision and accuracy tend to decrease. At this point, we can deduce that our hybrid recommender approaches could improve the recall without losing reached precision if $N$ is in a value between 10 and 20. -\begin{table}[htb] - \resizebox{0.6\linewidth}{!}{\centering % used for centering table - \begin{minipage}[b]{1.0\linewidth} - \begin{tabular}{c c c c c} % centered columns (4 columns) - \hline\hline %inserts double horizontal lines - Recommender & Precision & Recall & F1 & Accuracy \\ [0.5ex] % inserts table - %heading - \hline % inserts single horizontal line - Content-based (baseline) & 0.281 $\pm$ 0.052 & 0.041 $\pm$ 0.006 & 0.071 $\pm$ 0.010 & 0.666 $\pm$ 0.006 \\ % inserting body of the table - Hybrid (permutation EDA) & \textbf{0.363 $\pm$ 0.041} & \textbf{0.047 $\pm$ 0.008} & \textbf{0.084 $\pm$ 0.014} & \textbf{0.676 ± $\pm$ 0.007} \\ - Hybrid (continuous UMDA) & 0.302 $\pm$ 0.067 & 0.039 $\pm$ 0.011 & 0.070 $\pm$ 0.019 & 0.671 $\pm$ 0.010 \\ [1ex] % [1ex] adds vertical space - \hline %inserts single line - \end{tabular} - \end{minipage}} - - \caption{Evaluation of recommender systems (N=20)} % title of Table - \label{table:recn20} % is used to refer this table in the text -\end{table} - -%To achieve the best rendering both in printed proceedings and electronic proceedings, we -%strongly encourage you to use Times-Roman font. 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Delete the ``\vspace'' line, -% uncomment the preceding line ``\centerline...'' and replace ``imageX.ps'' -% with a suitable PostScript file name. -% ------------------------------------------------------------------------- -%\begin{figure}[htb] - -%\begin{minipage}[b]{1.0\linewidth} - %\centering - %\centerline{\includegraphics[width=8.5cm]{image1}} -% \vspace{2.0cm} - %\centerline{(a) Result 1}\medskip -%\end{minipage} -% -%\begin{minipage}[b]{.48\linewidth} - %\centering - %\centerline{\includegraphics[width=4.0cm]{image3}} -% \vspace{1.5cm} - %\centerline{(b) Results 3}\medskip -%\end{minipage} -%\hfill -%\begin{minipage}[b]{0.48\linewidth} - %\centering - %\centerline{\includegraphics[width=4.0cm]{image4}} -% \vspace{1.5cm} - %\centerline{(c) Result 4}\medskip -%\end{minipage} -% -%\caption{Example of placing a figure with experimental results.} -%\label{fig:res} -% -%\end{figure} - -\section{CONCLUSIONS AND FURTHER WORK} -\label{sec:refs} -The whole aim of our project has been the design and the implementation of an hybrid music recommender in order to mitigate the cold-start problem in content-based recommender systems. We investigated several types of hybridization in recommender systems to choose a suitable architecture for the available datasets. To represent real world users and raw waveforms, we decided to investigate and implement state-of-the-art techniques. Despite of the success in computer vision field, we found in our project that convolutional deep neural networks achieve similar results to a long-established content-based music genre classifier in music information retrieval field. Due to the natural selection concept associated to estimation of distribution algorithms, we investigated and considered these optimization techniques for modeling users' listening behavior in terms of probabilities of music genres from the songs they have listened. On the other hand, we found that a limited number of genres for song representation lead us to coarse predictions according to decision-based metrics. - -For the future, we have the intention to enhance our hybrid music recommender considering a wide range of music genres or latent vectors for item representation. We shall work on investigating several configurations of convolutional deep neural networks and different types of deep learning techniques, particularly, unsupervised learning approaches, for a better high-level representation of audio waveforms. In addition, we will continue investigating the fascinating estimation of distribution algorithms, considering another optimization functions, to model user profiles in recommender systems. Finally, we also consider the evaluation of the hybrid recommender with an online experiment. -%List and number all bibliographical references at the end of the -%paper. The references can be numbered in alphabetic order or in -%order of appearance in the document. When referring to them in -%the text, type the corresponding reference number in square -%brackets as shown at the end of this sentence \cite{C2}. An -%additional final page (the fifth page, in most cases) is -%allowed, but must contain only references to the prior -%literature. - -%\vfill\pagebreak - -% References should be produced using the bibtex program from suitable -% BiBTeX files (here: strings, refs, manuals). 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e8056343b36e -r c268fcd77848 paper IEEE/refs.bib --- a/paper IEEE/refs.bib Sat Jan 23 13:22:55 2016 -0500 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,151 +0,0 @@ -@InProceedings{C2, - author = "Jones, C.D. and Smith, A.B. and Roberts, E.F.", - title = "Article Title", - booktitle = "Proceedings Title", - organization = "IEEE", - year = "2003", - volume = "II", - pages = "803-806" -} - - -@incollection{melville2010recommender, - title={Recommender systems}, - author={Melville, Prem and Sindhwani, Vikas}, - booktitle={Encyclopedia of machine learning}, - pages={829--838}, - year={2010}, - publisher={Springer} -} - - -@incollection{Lops2011, - year={2011}, - isbn={978-0-387-85819-7}, - booktitle={Recommender Systems Handbook}, - editor={Ricci, Francesco and Rokach, Lior and Shapira, Bracha and Kantor, Paul B.}, - doi={10.1007/978-0-387-85820-3_3}, - title={Content-based Recommender Systems: State of the Art and Trends}, - url={http://dx.doi.org/10.1007/978-0-387-85820-3_3}, - publisher={Springer US}, - author={Lops, Pasquale and de Gemmis, Marco and Semeraro, Giovanni}, - pages={73-105}, - language={English} -} - - -@incollection{NIPS2013_5004, - title = {Deep content-based music recommendation}, - author = {van den Oord, Aaron and Dieleman, Sander and Schrauwen, Benjamin}, - booktitle = {Advances in Neural Information Processing Systems 26}, - editor = {C.J.C. Burges and L. Bottou and M. Welling and Z. Ghahramani and K.Q. Weinberger}, - pages = {2643--2651}, - year = {2013}, - publisher = {Curran Associates, Inc.}, - url = {http://papers.nips.cc/paper/5004-deep-content-based-music-recommendation.pdf} -} - - -@incollection{pelikan2015estimation, - title={Estimation of Distribution Algorithms}, - author={Pelikan, Martin and Hauschild, Mark W and Lobo, Fernando G}, - booktitle={Springer Handbook of Computational Intelligence}, - pages={899--928}, - year={2015}, - publisher={Springer} -} - - -@ARTICLE{Ding2015451, - author={Ding, C. and Ding, L. and Peng, W.}, - title={Comparison of effects of different learning methods on estimation of distribution algorithms}, - journal={Journal of Software Engineering}, - year={2015}, - volume={9}, - number={3}, - pages={451-468}, - doi={10.3923/jse.2015.451.468}, - note={}, - url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84924609049&partnerID=40&md5=e6419e97e218f8ef1600e3d21e6a9e36}, - document_type={Article}, - source={Scopus}, -} - - -@unpublished{Bengio-et-al-2015-Book, - title={Deep Learning}, - author={Yoshua Bengio and Ian J. Goodfellow and Aaron Courville}, - note={Book in preparation for MIT Press}, - url={http://www.iro.umontreal.ca/~bengioy/dlbook}, - year={2015} -} - - -@INPROCEEDINGS{Bertin-Mahieux2011, - author = {Thierry Bertin-Mahieux and Daniel P.W. Ellis and Brian Whitman and Paul Lamere}, - title = {The Million Song Dataset}, - booktitle = {{Proceedings of the 12th International Conference on Music Information - Retrieval ({ISMIR} 2011)}}, - year = {2011}, - owner = {thierry}, - timestamp = {2010.03.07} -} - - -@phdthesis {1242, - title = {Music Recommendation and Discovery in the Long Tail}, - year = {2008}, - school = {Universitat Pompeu Fabra}, - address = {Barcelona}, - abstract = {

- Music consumption is biased towards a few popular artists. For instance, in 2007 only 1\% of - all digital tracks accounted for 80\% of all sales. Similarly, 1,000 albums accounted for 50\% - of all album sales, and 80\% of all albums sold were purchased less than 100 times. There is - a need to assist people to filter, discover, personalise and recommend from the huge amount - of music content available along the Long Tail. -

-

- Current music recommendation algorithms try to - accurately predict what people demand to listen to. However, quite - often these algorithms tend to recommend popular -or well-known to the - user- music, decreasing the effectiveness of the recommendations. These - approaches focus on improving the accuracy of the recommendations. That - is, try to make - accurate predictions about what a user could listen to, or buy next, - independently of how - useful to the user could be the provided recommendations. -

-

- In this Thesis we stress the importance of the user{\textquoteright}s - perceived quality of the recommendations. We model the Long Tail curve - of artist popularity to predict -potentially- - interesting and unknown music, hidden in the tail of the popularity - curve. Effective recommendation systems should promote novel and - relevant material (non-obvious recommendations), taken primarily from - the tail of a popularity distribution. -

-

- The main contributions of this Thesis are: (i) a novel network-based approach for - recommender systems, based on the analysis of the item (or user) similarity graph, and the - popularity of the items, (ii) a user-centric evaluation that measures the user{\textquoteright}s relevance - and novelty of the recommendations, and (iii) two prototype systems that implement the - ideas derived from the theoretical work. Our findings have significant implications for - recommender systems that assist users to explore the Long Tail, digging for content they - might like. -

- }, - url = {http://mtg.upf.edu/static/media/PhD_ocelma.pdf}, - author = {Celma, \`{O}.} -} - - -@inproceedings{gallagher2007bayesian, - title={Bayesian inference in estimation of distribution algorithms}, - author={Gallagher, Marcus and Wood, Ian and Keith, Jonathan and Sofronov, George}, - booktitle={Evolutionary Computation, 2007. CEC 2007. IEEE Congress on}, - pages={127--133}, - year={2007}, - organization={IEEE} -} - - diff -r e8056343b36e -r c268fcd77848 paper IEEE/spconf.sty --- a/paper IEEE/spconf.sty Sat Jan 23 13:22:55 2016 -0500 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,252 +0,0 @@ -%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% -% -% File: spconf.sty (LaTeX Document style option "spconf") -% -% Usage: \documentclass{article} -% \usepackage{spconf} -% -% Or for LaTeX 2.09: -% Usage: \documentstyle[...,spconf,...]{article} -% -% Purpose: -% -% Style file for Signal Processing Society Conferences (ICASSP, ICIP). -% Features: -% - correct page size (175mm x 226mm) -% - twocolumn format -% - boldfaced, numbered, and centered section headings -% - correct subsection and subsubsection headings -% - use \title{xx} for title, will be typeset all uppercase -% - use \name{xx} for author name(s) only, will be typeset in italics -% - use \address{xx} for one address of all authors -% - use \twoauthors{author1}{address1}{author2}{address2} -% for two (or more) authors with two separate addresses -% - note: no need for \author nor \date -% - optional: can use \thanks{xx} within \name or \twoauthors, -% asterisk is not printed after name nor in footnote -% - optional: can use \sthanks{xx} after each name within \name or -% \twoauthors if different thanks for each author, -% footnote symbol will appear for each name and footnote -% - optional: use \ninept to typeset text in 9 pt; default is 10pt. -% -% Example of use for one or more authors at a common address and -% common support. 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Smith and C.D. Jones and E.F. Roberts", - title = "Article Title", - journal = "Journal", - year = "1920", - volume = "62", - pages = "291-294", - month = "January" -} - - -@ARTICLE{Yao2015453, - author={Yao, L. and Sheng, Q.Z. and Ngu, A.H.H. and Yu, J. and Segev, A.}, - title={Unified collaborative and content-based web service recommendation}, - journal={IEEE Transactions on Services Computing}, - year={2015}, - volume={8}, - number={3}, - pages={453-466}, - doi={10.1109/TSC.2014.2355842}, - art_number={6894179}, - note={}, - url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84932619562&partnerID=40&md5=4483a697e12fc53f620393586f85aebe}, - document_type={Article}, - source={Scopus}, -} - - -@ARTICLE{Burke2002331, - author={Burke, R.}, - title={Hybrid recommender systems: Survey and experiments}, - journal={User Modelling and User-Adapted Interaction}, - year={2002}, - volume={12}, - number={4}, - pages={331-370}, - doi={10.1023/A:1021240730564}, - note={}, - url={http://www.scopus.com/inward/record.url?eid=2-s2.0-0036959356&partnerID=40&md5=28885a102109be826507abc2435117a7}, - document_type={Article}, - source={Scopus}, -} - - -@ARTICLE{Yoshii2008435, - author={Yoshii, K. and Goto, M. and Komatani, K. and Ogata, T. and Okuno, H.G.}, - title={An efficient hybrid music recommender system using an incrementally trainable probabilistic generative model}, - journal={IEEE Transactions on Audio, Speech and Language Processing}, - year={2008}, - volume={16}, - number={2}, - pages={435-447}, - doi={10.1109/TASL.2007.911503}, - art_number={4432655}, - note={}, - url={http://www.scopus.com/inward/record.url?eid=2-s2.0-39649112098&partnerID=40&md5=6827f82844ae1da58a6fa95caf5092d9}, - document_type={Article}, - source={Scopus}, -} - - -@article{Santana:Bielza:Larrañaga:Lozano:Echegoyen:Mendiburu:Armañanzas:Shakya:2009:JSSOBK:v35i07, - author = "Roberto Santana and Concha Bielza and Pedro Larrañaga and Jose A. Lozano and Carlos Echegoyen and Alexander Mendiburu and Rubén Armañanzas and Siddartha Shakya", - title = "Mateda-2.0: A MATLAB Package for the Implementation and Analysis of Estimation of Distribution Algorithms", - journal = "Journal of Statistical Software", - volume = "35", - number = "7", - pages = "1--30", - day = "26", - month = "7", - year = "2010", - CODEN = "JSSOBK", - ISSN = "1548-7660", - bibdate = "2009-12-17", - URL = "http://www.jstatsoft.org/v35/i07", - accepted = "2009-12-17", - acknowledgement = "", - keywords = "", - submitted = "2009-04-15", -} - -@ARTICLE{Liang2014781, - author={Liang, T. and Liang, Y. and Fan, J. and Zhao, J.}, - title={A hybrid recommendation model based on estimation of distribution algorithms}, - journal={Journal of Computational Information Systems}, - year={2014}, - volume={10}, - number={2}, - pages={781-788}, - doi={10.12733/jcis9623}, - note={}, - url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84892865461&partnerID=40&md5=a2927d36b493e8ef4d1cdab3055fa68b}, - document_type={Article}, - source={Scopus}, -} - - -@article{DBLP:journals/corr/KereliukSL15, - author = {Corey Kereliuk and - Bob L. Sturm and - Jan Larsen}, - title = {Deep Learning and Music Adversaries}, - journal = {CoRR}, - volume = {abs/1507.04761}, - year = {2015}, - url = {http://arxiv.org/abs/1507.04761}, - timestamp = {Sun, 02 Aug 2015 18:42:02 +0200}, - biburl = {http://dblp.uni-trier.de/rec/bib/journals/corr/KereliukSL15}, - bibsource = {dblp computer science bibliography, http://dblp.org} -} - - -@ARTICLE{Tzanetakis2002293, - author={Tzanetakis, G. and Cook, P.}, - title={Musical genre classification of audio signals}, - journal={IEEE Transactions on Speech and Audio Processing}, - year={2002}, - volume={10}, - number={5}, - pages={293-302}, - doi={10.1109/TSA.2002.800560}, - note={}, - url={http://www.scopus.com/inward/record.url?eid=2-s2.0-0036648502&partnerID=40&md5=72d2fee186b42c9998f13415cbb79eea}, - document_type={Article}, - source={Scopus}, -} - - -@article{Hu2008263, - author={Hu, Y. and Volinsky, C. and Koren, Y.}, - title={Collaborative filtering for implicit feedback datasets}, - journal={Proceedings - IEEE International Conference on Data Mining, ICDM}, - year={2008}, - pages={263-272}, - doi={10.1109/ICDM.2008.22}, - art_number={4781121}, - note={}, - url={http://www.scopus.com/inward/record.url?eid=2-s2.0-67049164166&partnerID=40&md5=01238b08208962fd0fdcc7503fa3af99}, - document_type={Conference Paper}, - source={Scopus}, -} - - -@article{Yin2012896, - author={Yin, H. and Cui, B. and Li, J. and Yao, J. and Chen, C.}, - title={Challenging the long tail recommendation}, - journal={Proceedings of the VLDB Endowment}, - year={2012}, - volume={5}, - number={9}, - pages={896-907}, - note={}, - url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84863735354&partnerID=40&md5=2bd887772ba832fbbb4631afd25514d9}, - document_type={Article}, - source={Scopus}, -} - -@article{Dai20141760, - author={Dai, C. and Qian, F. and Jiang, W. and Wang, Z. and Wu, Z.}, - title={A personalized recommendation system for netease dating site}, - journal={Proceedings of the VLDB Endowment}, - year={2014}, - volume={7}, - number={13}, - pages={1760-1765}, - note={}, - url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84905828317&partnerID=40&md5=90fbd8b20ad39757895bdee3ca58f459}, - document_type={Article}, - source={Scopus}, -} - - diff -r e8056343b36e -r c268fcd77848 paper IEEE/texput.log --- a/paper IEEE/texput.log Sat Jan 23 13:22:55 2016 -0500 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,21 +0,0 @@ -This is pdfTeX, Version 3.14159265-2.6-1.40.16 (TeX Live 2015) (preloaded format=pdflatex 2015.7.15) 21 SEP 2015 15:35 -entering extended mode - restricted \write18 enabled. - %&-line parsing enabled. -**refs.tex - -! Emergency stop. -<*> refs.tex - -*** (job aborted, file error in nonstop mode) - - -Here is how much of TeX's memory you used: - 3 strings out of 493089 - 99 string characters out of 6134842 - 53199 words of memory out of 5000000 - 3590 multiletter control sequences out of 15000+600000 - 3640 words of font info for 14 fonts, out of 8000000 for 9000 - 1141 hyphenation exceptions out of 8191 - 0i,0n,0p,1b,6s stack positions out of 5000i,500n,10000p,200000b,80000s -! ==> Fatal error occurred, no output PDF file produced! diff -r e8056343b36e -r c268fcd77848 paper/chiliguano.aux --- a/paper/chiliguano.aux Sat Jan 23 13:22:55 2016 -0500 +++ b/paper/chiliguano.aux Sat Jan 23 13:34:38 2016 -0500 @@ -20,6 +20,7 @@ \citation{Santana:Bielza:Larrañaga:Lozano:Echegoyen:Mendiburu:Armañanzas:Shakya:2009:JSSOBK:v35i07} \citation{Ding2015451} \citation{Bertin-Mahieux2011} +\citation{1242} \@writefile{toc}{\contentsline {section}{\numberline {3} METHODOLOGY}{2}} \newlabel{sec:format}{{3}{2}} \@writefile{lof}{\contentsline {figure}{\numberline {1}{\ignorespaces Flowchart of an EDA presented in\nobreakspace {}\cite {Ding2015451}}}{2}} @@ -27,7 +28,6 @@ \@writefile{lof}{\contentsline {figure}{\numberline {2}{\ignorespaces Diagram of hybrid music recommender}}{2}} \newlabel{fig:hybrid}{{2}{2}} \@writefile{toc}{\contentsline {subsection}{\numberline {3.1} Data collection}{2}} -\citation{1242} \citation{NIPS2013_5004} \citation{Tzanetakis2002293} \citation{DBLP:journals/corr/KereliukSL15} @@ -35,7 +35,7 @@ \citation{gallagher2007bayesian} \citation{1242} \@writefile{toc}{\contentsline {subsection}{\numberline {3.2} Probability of music genre representation}{3}} -\@writefile{toc}{\contentsline {subsection}{\numberline {3.3} User profile modelling with EDAs}{3}} +\@writefile{toc}{\contentsline {subsection}{\numberline {3.3} User profile modeling with EDAs}{3}} \newlabel{eq:fitness}{{1}{3}} \@writefile{toc}{\contentsline {subsection}{\numberline {3.4} Top-N songs recommendation}{3}} \@writefile{toc}{\contentsline {section}{\numberline {4} EXPERIMENTS}{3}} @@ -43,14 +43,6 @@ \@writefile{toc}{\contentsline {subsection}{\numberline {4.1} Top-N evaluation}{3}} \@writefile{toc}{\contentsline {section}{\numberline {5} RESULTS}{3}} \newlabel{sec:typestyle}{{5}{3}} -\@writefile{lot}{\contentsline {table}{\numberline {1}{\ignorespaces Evaluation of recommender systems (N=5)}}{4}} -\newlabel{table:recn5}{{1}{4}} -\@writefile{lot}{\contentsline {table}{\numberline {2}{\ignorespaces Evaluation of recommender systems (N=10)}}{4}} -\newlabel{table:recn10}{{2}{4}} -\@writefile{lot}{\contentsline {table}{\numberline {3}{\ignorespaces Evaluation of recommender systems (N=20)}}{4}} -\newlabel{table:recn20}{{3}{4}} -\@writefile{toc}{\contentsline {section}{\numberline {6} CONCLUSIONS AND FURTHER WORK}{4}} -\newlabel{sec:refs}{{6}{4}} \bibstyle{IEEEbib} \bibdata{strings,refs} \bibcite{melville2010recommender}{1} @@ -62,6 +54,15 @@ \bibcite{Lops2011}{7} \bibcite{Yoshii2008435}{8} \bibcite{NIPS2013_5004}{9} +\@writefile{lot}{\contentsline {table}{\numberline {1}{\ignorespaces Evaluation of recommender systems (N=5)}}{4}} +\newlabel{table:recn5}{{1}{4}} +\@writefile{lot}{\contentsline {table}{\numberline {2}{\ignorespaces Evaluation of recommender systems (N=10)}}{4}} +\newlabel{table:recn10}{{2}{4}} +\@writefile{lot}{\contentsline {table}{\numberline {3}{\ignorespaces Evaluation of recommender systems (N=20)}}{4}} +\newlabel{table:recn20}{{3}{4}} +\@writefile{toc}{\contentsline {section}{\numberline {6} CONCLUSIONS AND FURTHER WORK}{4}} +\newlabel{sec:refs}{{6}{4}} +\@writefile{toc}{\contentsline {section}{\numberline {7} References}{4}} \bibcite{Bengio-et-al-2015-Book}{10} \bibcite{Bertin-Mahieux2011}{11} \bibcite{pelikan2015estimation}{12} @@ -72,4 +73,3 @@ \bibcite{DBLP:journals/corr/KereliukSL15}{17} \bibcite{Liang2014781}{18} \bibcite{gallagher2007bayesian}{19} -\@writefile{toc}{\contentsline {section}{\numberline {7} References}{5}} diff -r e8056343b36e -r c268fcd77848 paper/chiliguano.bbl --- a/paper/chiliguano.bbl Sat Jan 23 13:22:55 2016 -0500 +++ b/paper/chiliguano.bbl Sat Jan 23 13:34:38 2016 -0500 @@ -22,17 +22,20 @@ \bibitem{Hu2008263} Y.~Hu, C.~Volinsky, and Y.~Koren, \newblock ``Collaborative filtering for implicit feedback datasets,'' -\newblock 2008, pp. 263--272. +\newblock {\em Proceedings - IEEE International Conference on Data Mining, + ICDM}, pp. 263--272, 2008. \bibitem{Yin2012896} H.~Yin, B.~Cui, J.~Li, J.~Yao, and C.~Chen, \newblock ``Challenging the long tail recommendation,'' -\newblock 2012, vol.~5, pp. 896--907. +\newblock {\em Proceedings of the VLDB Endowment}, vol. 5, no. 9, pp. 896--907, + 2012. \bibitem{Dai20141760} C.~Dai, F.~Qian, W.~Jiang, Z.~Wang, and Z.~Wu, \newblock ``A personalized recommendation system for netease dating site,'' -\newblock 2014, vol.~7, pp. 1760--1765. +\newblock {\em Proceedings of the VLDB Endowment}, vol. 7, no. 13, pp. + 1760--1765, 2014. \bibitem{Lops2011} Pasquale Lops, Marco de~Gemmis, and Giovanni Semeraro, diff -r e8056343b36e -r c268fcd77848 paper/chiliguano.blg --- a/paper/chiliguano.blg Sat Jan 23 13:22:55 2016 -0500 +++ b/paper/chiliguano.blg Sat Jan 23 13:34:38 2016 -0500 @@ -4,50 +4,44 @@ The style file: IEEEbib.bst Database file #1: strings.bib Database file #2: refs.bib -Warning--empty booktitle in Hu2008263 -Warning--empty booktitle in Yin2012896 -Warning--can't use both volume and number fields in Yin2012896 -Warning--empty booktitle in Dai20141760 -Warning--can't use both volume and number fields in Dai20141760 You've used 19 entries, 1838 wiz_defined-function locations, 587 strings with 7388 characters, -and the built_in function-call counts, 5659 in all, are: -= -- 503 +and the built_in function-call counts, 5356 in all, are: += -- 477 > -- 268 -< -- 10 +< -- 6 + -- 95 - -- 76 -* -- 451 -:= -- 854 +* -- 445 +:= -- 842 add.period$ -- 24 call.type$ -- 19 change.case$ -- 18 chr.to.int$ -- 0 -cite$ -- 24 -duplicate$ -- 257 -empty$ -- 521 +cite$ -- 19 +duplicate$ -- 228 +empty$ -- 487 format.name$ -- 76 -if$ -- 1312 +if$ -- 1224 int.to.chr$ -- 0 int.to.str$ -- 19 missing$ -- 17 newline$ -- 98 num.names$ -- 23 -pop$ -- 63 +pop$ -- 50 preamble$ -- 1 purify$ -- 0 quote$ -- 0 -skip$ -- 140 +skip$ -- 104 stack$ -- 0 -substring$ -- 393 -swap$ -- 98 -text.length$ -- 10 +substring$ -- 377 +swap$ -- 74 +text.length$ -- 6 text.prefix$ -- 0 top$ -- 0 type$ -- 0 -warning$ -- 5 -while$ -- 49 +warning$ -- 0 +while$ -- 47 width$ -- 21 -write$ -- 214 -(There were 5 warnings) +write$ -- 215 diff -r e8056343b36e -r c268fcd77848 paper/chiliguano.log --- a/paper/chiliguano.log Sat Jan 23 13:22:55 2016 -0500 +++ b/paper/chiliguano.log Sat Jan 23 13:34:38 2016 -0500 @@ -1,4 +1,4 @@ -This is pdfTeX, Version 3.14159265-2.6-1.40.16 (TeX Live 2015) (preloaded format=pdflatex 2015.7.15) 26 SEP 2015 13:04 +This is pdfTeX, Version 3.14159265-2.6-1.40.16 (TeX Live 2015) (preloaded format=pdflatex 2015.7.15) 23 JAN 2016 13:34 entering extended mode restricted \write18 enabled. %&-line parsing enabled. @@ -218,16 +218,18 @@ Package pdftex.def Info: hybrid.png used on input line 97. 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PDF statistics: 70 PDF objects out of 1000 (max. 8388607) 47 compressed objects within 1 object stream diff -r e8056343b36e -r c268fcd77848 paper/chiliguano.pdf Binary file paper/chiliguano.pdf has changed diff -r e8056343b36e -r c268fcd77848 paper/chiliguano.synctex.gz Binary file paper/chiliguano.synctex.gz has changed diff -r e8056343b36e -r c268fcd77848 paper/chiliguano.tex --- a/paper/chiliguano.tex Sat Jan 23 13:22:55 2016 -0500 +++ b/paper/chiliguano.tex Sat Jan 23 13:34:38 2016 -0500 @@ -44,7 +44,7 @@ \maketitle % \begin{abstract} -Internet resources available today, including songs, albums, playlists or podcasts, that a user cannot discover if there is not a tool to filter the items that the user might consider relevant. Several recommendation techniques have been developed since the Internet explosion to achieve this filtering task. In an attempt to recommend relevant songs to users, we propose an hybrid recommender that considers real-world users information and high-level representation for audio data. We use a deep learning technique, convolutional deep neural networks, to represent an audio segment in a n-dimensional vector, whose dimensions define the probability of the segment to belong to a specific music genre. To capture the listening behavior of a user, we investigate a state-of-the-art technique, estimation of distribution algorithms. The designed hybrid music recommender outperforms the predictions compared with a traditional content-based recommender. +Internet resources available today, including songs, albums, playlists or podcasts, that a user cannot discover require a tool for filtering the items that a user might consider relevant. Several recommendation techniques have been developed since the Internet explosion to achieve this filtering task. In an attempt to recommend relevant songs to users, we propose an hybrid music recommender system that considers real-world users information and high-level representation for audio data. The system uses a convolutional deep neural network to represent audio segments by genre and uses estimation of distribution algorithms to represent the user listening behavior. Finally, content-based filtering is employed to generate Top-N recommendations. Our proposed hybrid music recommender presents a convincing performance improvement over the content-based baseline model. \end{abstract} % \begin{keywords} @@ -57,7 +57,7 @@ Recommender systems~\cite{melville2010recommender} are software or technical facilities to provide items suggestions or predict customer preferences by using prior user information. These systems play an important role in commercial applications to increase sales and improve user satisfaction. In general, recommender systems can be categorized in two major groups: collaborative filtering (CF) and content-based (CB) filtering. In CF~\cite{Yao2015453, Burke2002331}, a \emph{m$\times$n} rating matrix represents the relationships between \textit{m} users and \textit{n} items and recommendations are based on the computed similarities between rows (for users) or columns (for items). CF can be further subdivided in neighborhood models~\cite{Hu2008263}. The recommendation process in CF is independent of the item features. However, CF ignores the items in the \textit{long tail}~\cite{Yin2012896} and it is unable to handle the \textit{cold-start}~\cite{Dai20141760} problem. On the other hand, CB filtering~\cite{Lops2011} is based on the analysis of the features that describe the items. The recommendation component consists in matching up the attributes of the items that a user has already rated, usually referred as the \textit{user profile}, against the attributes of a previously unseen products to produce a list of \emph{top-N} recommendations. The recommendation process in CB is entirely based on the attributes of the items, thus, the recommendations produced for each user is independent from the other users. Also, a CB recommender allows to recommend items that do not have any rating, therefore, the effects of cold-start problem can be diminished. However, the quality of recommendations from CB techniques depends on the size of historical dataset. A hybrid recommender~\cite{Burke2002331} system is usually developed through the combination of CF and CB recommendation techniques to boost the advantages of CF by considering the feedback (rating) of users and the advantages of CB by taking into count the item attributes. Methods of combining recommendation techniques to accomplish hybridization can be classified in weighted, switching, mixed, feature combination, cascade, feature augmentation and meta-level methods. -In Section~\ref{sec:background}, we revise related work in music recommender systems. In Section~\ref{sec:format}, we present our proposed hybrid recommendation approach and describe the stages and algorithms in detail. In Section~\ref{sec:pagestyle}, we present the experiments and evaluation protocols to assess the performance of the hybrid recommender. In Section~\ref{sec:typestyle}, we discuss and analyze the results from the conducted experiments to evaluate the proposed hybrid music recommender. We conclude in Section~\ref{sec:refs}, we present the conclusions and some thoughts for further research. +In Section~\ref{sec:background}, we revise related work in music recommender systems. In Section~\ref{sec:format}, we present our proposed hybrid recommendation approach and its stages, also we describe the music genre representation of audio and user profile modeling. In Section~\ref{sec:pagestyle}, we present the experiments and evaluation protocols to assess the performance of the hybrid recommender. In Section~\ref{sec:typestyle}, we discuss and analyze the results from the conducted experiments to evaluate the proposed hybrid music recommender. We conclude in Section~\ref{sec:refs}, we present the conclusions and some thoughts for further research. \section{Background} @@ -67,7 +67,7 @@ Inspired by natural selection of species, estimation of distribution algorithms (EDAs)~\cite{pelikan2015estimation, Ding2015451, Santana:Bielza:Larrañaga:Lozano:Echegoyen:Mendiburu:Armañanzas:Shakya:2009:JSSOBK:v35i07} are optimization techniques that estimate a probabilistic model from a sample of promising individuals. The sample is used to generate a new population that satisfy a criteria, i.e., minimization of a \textit{fitness} function. In Figure~\ref{fig:eda}, we show the general flowchart of an EDA. Regarding to the types of distributions that an EDA are able to capture can be categorized in four broad groups: discrete variables EDA, permutation EDA, real-valued vectors EDA and genetic programming EDA. -In this paper, we present a hybrid music recommender to mitigate the cold-start problem in recommendation strategies. First, we use CNNs to describe the time-frequency content of an audio clip with a n-dimensional vector, whose dimensions represent the probability of a clip to belong to an specific music genre. Second, as a primary contribution, EDAs are investigated to model user profiles in terms of probabilities of music genres preferences. The algorithms use play count and the content vector of each song in the user's collection to optimize the profile. In addition, each dimension in the content vector is treated as a discrete and continuous variable for evaluation purposes. To our knowledge, this is the first approach that uses a continuous EDA for user profile modeling in recommender systems. +In this paper, we present a hybrid music recommender to mitigate the cold-start problem in recommendation strategies. First, we use CNNs to describe the time-frequency content of an audio clip with a n-dimensional vector, whose dimensions represent the probability of a clip to belong to an specific music genre. Second, as a primary contribution, EDAs are investigated to model user profiles in terms of probabilities of music genres preferences. The algorithms use play count and the content vector of each song in the user's collection to optimize the profile. To our knowledge, this is the first approach that uses a continuous EDA for user profile modeling in recommender systems. \begin{figure}[htb] @@ -119,7 +119,7 @@ %In our approach, we describe the songs in terms of n-dimensional vectors. Each dimension in the vector represent the probability of the song to belong to a music genre. The probability estimation is obtained from a music classifier implemented with a deep learning technique. -\subsection{User profile modelling with EDAs} +\subsection{User profile modeling with EDAs} In our project, we model each user profile following the EDA approach of~\cite{Liang2014781} by minimizing a fitness function given by Equation~\eqref{eq:fitness} \ @@ -137,12 +137,12 @@ \section{EXPERIMENTS} \label{sec:pagestyle} -The hybrid music recommender system proposed in this project is evaluated through an offline experiment and the results are presented with decision based metrics~\cite{1242}. +The hybrid music recommender system proposed in this project is evaluated through an off-line experiment and the results are presented with decision based metrics: Precision, recall, F1 measure and accuracy~\cite{1242}. The reduced taste profile dataset is split in a training and a test set. For each user in the dataset, a random sample corresponding to 20 \% of the total number of ratings is assigned to the test set, and the rest 80 \% is assigned to the training set. The split process is iterated for 10 times, resulting in a total of 10 training and 10 test sets. \subsection{Top-N evaluation} -For each song in the user test set, we look up if the song is included or not in the list of top-N recommendations. If the test song is in the top-N recommendation and if the rating of the test song is above the threshold (rating $>$ 2), we count as a true positive, otherwise is counted as a false positive. If the test song is not in the top-N recommendation and if the rating of the test song is above the threshold, we count as a false negative, otherwise is counted as a true positive. +For each song in the user test set, we look up if the song is included or not in the top-N recommendations. If the song rating is above the threshold (we find a rating $\geq$ 3 out of 5 suitable) and the song is ranked in the top-N recommendations, we count as a true positive, otherwise, if the song is not included in the recommendations we count as false negative. On the other hand, if the song rating is below the threshold and the song appears in the top-N recommendations, we count as a false positive, otherwise, if the song is not included on the recommendations, we count as a false negative. %The paper title (on the first page) should begin 1.38 inches (35 mm) from the %top edge of the page, centered, completely capitalized, and in Times 14-point, @@ -154,7 +154,7 @@ \section{RESULTS} \label{sec:typestyle} -In general, the results demonstrate the hybrid music recommender based on a permutation EDA presents a better performance compared with both the CB recommender and the hybrid approach based on a continuous EDA. Nevertheless, the recall values are lower in all cases. In Table~\ref{table:recn5}, the results of top-5 recommendation are shown. +In general, the results of the top-N evaluation demonstrate that the hybrid music recommender based on a permutation EDA presents a better performance compared with both the CB recommender and the hybrid approach based on a continuous EDA. In Table~\ref{table:recn5}, the results of top-5 recommendation are shown. \begin{table}[htb] \resizebox{0.6\linewidth}{!}{\centering % used for centering table \begin{minipage}[b]{1.0\linewidth} @@ -174,7 +174,7 @@ \label{table:recn5} % is used to refer this table in the text \end{table} -In Table~\ref{table:recn10}, the results of top-10 songs recommendation are shown. In this case, the precision value improve for the CB recommender. The accuracy values for all recommender systems tend to decrease. +In Table~\ref{table:recn10}, the results of top-10 songs recommendation are shown. In this case, the precision value improves for the CB recommender. The accuracy values for all recommender systems tend to decrease. \begin{table}[htb] \resizebox{0.6\linewidth}{!}{\centering % used for centering table \begin{minipage}[b]{1.0\linewidth} @@ -265,9 +265,9 @@ \section{CONCLUSIONS AND FURTHER WORK} \label{sec:refs} -The whole aim of our project has been the design and the implementation of an hybrid music recommender in order to mitigate the cold-start problem in content-based recommender systems. We investigated several types of hybridization in recommender systems to choose a suitable architecture for the available datasets. To represent real world users and raw waveforms, we decided to investigate and implement state-of-the-art techniques. Despite of the success in computer vision field, we found in our project that convolutional deep neural networks achieve similar results to long-established music genre classifier approaches in music information retrieval field. Due to the natural selection concept associated to estimation of distribution algorithms, we investigated and considered these optimization techniques for modeling users' listening behavior in terms of probabilities of music genres from the songs in they listened. On the other hand, we found that a limited number of genres for song representation lead us to coarse predictions according to decision-based metrics. +The whole aim of our project has been the design and the implementation of an hybrid music recommender in order to mitigate the cold-start problem in content-based recommender systems. We investigated several types of hybridization in recommender systems to choose a suitable architecture for the available datasets. To represent real world users and raw waveforms, we decided to investigate and implement state-of-the-art techniques. Despite of the success in computer vision field, we found in our project that convolutional deep neural networks achieve similar results to a long-established content-based music genre classifier in music information retrieval field. Due to the natural selection concept associated to estimation of distribution algorithms, we investigated and considered these optimization techniques for modeling users' listening behavior in terms of probabilities of music genres from the songs they have listened. On the other hand, we found that a limited number of genres for song representation lead us to coarse predictions according to decision-based metrics. -For the future, we have the intention to enhance our hybrid music recommender considering a wide range of music genres or latent vectors for item representation. We shall work on investigating several configurations of convolutional deep neural networks and different types of deep learning techniques, particularly, unsupervised learning approaches, for a better high-level representation of audio waveforms. In addition, we will continue investigating the fascinating estimation of distribution algorithms, considering another fitness functions to optimise, to model user profiles in recommender systems. Finally, we also consider the evaluation of hybrid recommender with an online experiment. +For the future, we have the intention to enhance our hybrid music recommender considering a wide range of music genres or latent vectors for item representation. We shall work on investigating several configurations of convolutional deep neural networks and different types of deep learning techniques, particularly, unsupervised learning approaches, for a better high-level representation of audio waveforms. In addition, we will continue investigating the fascinating estimation of distribution algorithms, considering another optimization functions, to model user profiles in recommender systems. Finally, we also consider the evaluation of the hybrid recommender with an online experiment. %List and number all bibliographical references at the end of the %paper. The references can be numbered in alphabetic order or in %order of appearance in the document. When referring to them in @@ -277,7 +277,7 @@ %allowed, but must contain only references to the prior %literature. -\vfill\pagebreak +%\vfill\pagebreak % References should be produced using the bibtex program from suitable % BiBTeX files (here: strings, refs, manuals). 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Smith, C.D. Jones, and E.F. Roberts, -\newblock ``Article title,'' -\newblock {\em Journal}, vol. 62, pp. 291--294, January 1920. - -\bibitem{C2} -C.D. Jones, A.B. Smith, and E.F. Roberts, -\newblock ``Article title,'' -\newblock in {\em Proceedings Title}. 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author={Hu, Y. and Volinsky, C. and Koren, Y.}, - title={Collaborative filtering for implicit feedback datasets}, - journal={Proceedings - IEEE International Conference on Data Mining, ICDM}, - year={2008}, - pages={263-272}, - doi={10.1109/ICDM.2008.22}, - art_number={4781121}, - note={}, - url={http://www.scopus.com/inward/record.url?eid=2-s2.0-67049164166&partnerID=40&md5=01238b08208962fd0fdcc7503fa3af99}, - document_type={Conference Paper}, - source={Scopus}, -} - - @incollection{Lops2011, year={2011}, isbn={978-0-387-85819-7}, @@ -49,36 +34,6 @@ } -@CONFERENCE{Yin2012896, - author={Yin, H. and Cui, B. and Li, J. and Yao, J. and Chen, C.}, - title={Challenging the long tail recommendation}, - journal={Proceedings of the VLDB Endowment}, - year={2012}, - volume={5}, - number={9}, - pages={896-907}, - note={}, - url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84863735354&partnerID=40&md5=2bd887772ba832fbbb4631afd25514d9}, - document_type={Article}, - source={Scopus}, -} - - -@CONFERENCE{Dai20141760, - author={Dai, C. and Qian, F. and Jiang, W. and Wang, Z. and Wu, Z.}, - title={A personalized recommendation system for netease dating site}, - journal={Proceedings of the VLDB Endowment}, - year={2014}, - volume={7}, - number={13}, - pages={1760-1765}, - note={}, - url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84905828317&partnerID=40&md5=90fbd8b20ad39757895bdee3ca58f459}, - document_type={Article}, - source={Scopus}, -} - - @incollection{NIPS2013_5004, title = {Deep content-based music recommendation}, author = {van den Oord, Aaron and Dieleman, Sander and Schrauwen, Benjamin}, diff -r e8056343b36e -r c268fcd77848 paper/strings.bib --- a/paper/strings.bib Sat Jan 23 13:22:55 2016 -0500 +++ b/paper/strings.bib Sat Jan 23 13:34:38 2016 -0500 @@ -126,3 +126,47 @@ } +@article{Hu2008263, + author={Hu, Y. and Volinsky, C. and Koren, Y.}, + title={Collaborative filtering for implicit feedback datasets}, + journal={Proceedings - IEEE International Conference on Data Mining, ICDM}, + year={2008}, + pages={263-272}, + doi={10.1109/ICDM.2008.22}, + art_number={4781121}, + note={}, + url={http://www.scopus.com/inward/record.url?eid=2-s2.0-67049164166&partnerID=40&md5=01238b08208962fd0fdcc7503fa3af99}, + document_type={Conference Paper}, + source={Scopus}, +} + + +@article{Yin2012896, + author={Yin, H. and Cui, B. and Li, J. and Yao, J. and Chen, C.}, + title={Challenging the long tail recommendation}, + journal={Proceedings of the VLDB Endowment}, + year={2012}, + volume={5}, + number={9}, + pages={896-907}, + note={}, + url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84863735354&partnerID=40&md5=2bd887772ba832fbbb4631afd25514d9}, + document_type={Article}, + source={Scopus}, +} + +@article{Dai20141760, + author={Dai, C. and Qian, F. and Jiang, W. and Wang, Z. and Wu, Z.}, + title={A personalized recommendation system for netease dating site}, + journal={Proceedings of the VLDB Endowment}, + year={2014}, + volume={7}, + number={13}, + pages={1760-1765}, + note={}, + url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84905828317&partnerID=40&md5=90fbd8b20ad39757895bdee3ca58f459}, + document_type={Article}, + source={Scopus}, +} + +