changeset 51:23b8bfe481b1

my final changes too

--- a/musicweb.tex	Sun May 01 05:48:08 2016 +0100
+++ b/musicweb.tex	Sun May 01 10:36:35 2016 +0100
@@ -293,6 +293,12 @@
 
 While automatic feature extraction has significantly enhanced organisation and categorisation of large music collections, it is still rather challenging to derive high level semantic information relating to mood or genre. Complementing signal processing and machine learning methods with crowd-sourced social tagging data from platforms like Last.fm can enrich and inform understanding of general listening habits and connections between artists.
 Mood-based similarity is another experimental enhancement to MusicWeb. This method involves using the Semantic Web version of ILM10K music mood dataset that consists of over 4000 unique artists. The dataset is based on crowd-sourced mood tag statistics from Last.fm users, which have been transformed to numerical coordinates in a Cartesian space. Every track in the collection is associated to 2-dimensional coordinates reflecting energy and pleasantness respectively. The similarity between artists in this case is measured by first calculating the location of the target artist in the mood space by averaging the coordinates of all the associated tracks. The same procedure is repeated for all other artists which then enables computing Manhattan distances between the target from the rest and using the ranking as similarity metric. This process is illustrated by the example SPARQL query in Listing \ref{lst:sparql1}.
+
+
+\section{Discussion}\label{sec:discussion}
+Interacting with MusicWeb can be a surprising experience. Often, the artists visited are similar enough. It is not unexpected that Rihanna and Chris Brown are linked because they are both mentioned in the same news item. Or, for instance, Schumman, Von Weber and Berlioz are all identified in the same musicology paper. It often happens, however, that the user begins by searching an artist and, following some of the links offered, ends up listening to a completely different style of music. One such journey, for example, started with Madonna. By following links only suggested by the fact that both artists appear in the same text the user is directed to Theodor W. Adorno, and then to Gustav Mahler, and finally to Pierre Boulez. Taking two steps back, from Adorno the user can choose to visit Niezstche's page, both being German composers who are also philosophers, or to Albert Einstein's, since both are Jewish people who migrated to the U.S. fleeing Nazi Germany. One final example of following semantic links: a user who searches for Bach can then go to Mozart's page. From there he can proceed to Glenn Gould's, on to Thelonius Monk and finally John Coltrane.
+This kind of discovery path would be very unlikely in any recommender system based on user profile data.
+
 \vspace{-10pt}
 \noindent\begin{minipage}[!ht]{\textwidth}
 	\begin{lstlisting}[ style = sparql, label=lst:sparql1, tabsize=4,  caption={example SPARQL query to retrieve similar artists from the ILM10K mood dataset by Manhattan distance of valence-arousal coordinates.} ]
@@ -326,14 +332,14 @@
 \end{minipage}
 
 
-\section{Discussion}\label{sec:discussion}
-Interacting with MusicWeb can be a surprising experience. Often, the artists visited are similar enough. It is not unexpected that Rihanna and Chris Brown are linked because they are both mentioned in the same news item. Or, for instance, Schumman, Von Weber and Berlioz are all identified in the same musicology paper. It often happens, however, that the user begins by searching an artist and, following some of the links offered, ends up listening to a completely different style of music. One such journey, for example, started with Madonna. By following links only suggested by the fact that both artists appear in the same text the user is directed to Theodor W. Adorno, and then to Gustav Mahler, and finally to Pierre Boulez. Taking two steps back, from Adorno the user can choose to visit Niezstche's page, both being German composers who are also philosophers, or to Albert Einstein's, since both are Jewish people who migrated to the U.S. fleeing Nazi Germany. One final example of following semantic links: a user who searches for Bach can then go to Mozart's page. From there he can proceed to Glenn Gould's, on to Thelonius Monk and finally John Coltrane.
-This kind of discovery path would be very unlikely in any recommender system based on user profile data.
+% \section{Conclusions}\label{sec:conclusions}
 
-\section{Conclusions}\label{sec:conclusions}
+\section{Conclusions and Future work}\label{sec:conclusions}
 
-\subsection{Future work}
-MusicWeb is an emerging application being developed at a research center to explore the possibilities of linked data-based music discovery. As such, its user base is at the moment limited to member of the research group (roughly 30 people, students and staff). Hence there are no major technical infrascuture requirements which cannot be supported by the university. This does not mean that we do not intend to utilise very large datasets. It was conceived and it is being developed mainly as a research tool. Our aim is to gather in one application various different approaches to music discovery and how they can benefit from linked music metadata. Our next steps are directed toward evaluating its potential acceptance by end users. It would be of great value to us to find out which linking methods listeners find most appealing or interesting, and which they would use more often.
+% \subsection{Future work}
+MusicWeb is an emerging application being developed at a research centre to explore the possibilities of linked data-based music discovery and tested with a small user base of student and staff. % As such, its user base is at the moment limited to member of the research group (roughly 30 people, students and staff). Hence there are no major technical infrastructure requirements which cannot be supported by the university. This does not mean that we do not intend to utilise very large datasets. It was conceived and it is being developed mainly as a research tool. 
+% actually C4DM is over 70 people!!
+Our aim is to gather in one application various different approaches to music discovery and how they can benefit from linked music metadata. Our next steps are directed toward evaluating its potential acceptance by end users. It would be of great value to us to find out which linking methods listeners find most appealing or interesting, and which they would use more often.
 As to the different methods of linking music metadata, our next steps will be:
 \begin{itemize}
 \item In literature-based linking: