p@32: @InProceedings{C2,
p@32: author = "Jones, C.D. and Smith, A.B. and Roberts, E.F.",
p@32: title = "Article Title",
p@32: booktitle = "Proceedings Title",
p@32: organization = "IEEE",
p@32: year = "2003",
p@32: volume = "II",
p@32: pages = "803-806"
p@32: }
p@32:
p@32:
p@32: @incollection{melville2010recommender,
p@32: title={Recommender systems},
p@32: author={Melville, Prem and Sindhwani, Vikas},
p@32: booktitle={Encyclopedia of machine learning},
p@32: pages={829--838},
p@32: year={2010},
p@32: publisher={Springer}
p@32: }
p@32:
p@32:
p@32: @incollection{Lops2011,
p@32: year={2011},
p@32: isbn={978-0-387-85819-7},
p@32: booktitle={Recommender Systems Handbook},
p@32: editor={Ricci, Francesco and Rokach, Lior and Shapira, Bracha and Kantor, Paul B.},
p@32: doi={10.1007/978-0-387-85820-3_3},
p@32: title={Content-based Recommender Systems: State of the Art and Trends},
p@32: url={http://dx.doi.org/10.1007/978-0-387-85820-3_3},
p@32: publisher={Springer US},
p@32: author={Lops, Pasquale and de Gemmis, Marco and Semeraro, Giovanni},
p@32: pages={73-105},
p@32: language={English}
p@32: }
p@32:
p@32:
p@32: @incollection{NIPS2013_5004,
p@32: title = {Deep content-based music recommendation},
p@32: author = {van den Oord, Aaron and Dieleman, Sander and Schrauwen, Benjamin},
p@32: booktitle = {Advances in Neural Information Processing Systems 26},
p@32: editor = {C.J.C. Burges and L. Bottou and M. Welling and Z. Ghahramani and K.Q. Weinberger},
p@32: pages = {2643--2651},
p@32: year = {2013},
p@32: publisher = {Curran Associates, Inc.},
p@32: url = {http://papers.nips.cc/paper/5004-deep-content-based-music-recommendation.pdf}
p@32: }
p@32:
p@32:
p@32: @incollection{pelikan2015estimation,
p@32: title={Estimation of Distribution Algorithms},
p@32: author={Pelikan, Martin and Hauschild, Mark W and Lobo, Fernando G},
p@32: booktitle={Springer Handbook of Computational Intelligence},
p@32: pages={899--928},
p@32: year={2015},
p@32: publisher={Springer}
p@32: }
p@32:
p@32:
p@32: @ARTICLE{Ding2015451,
p@32: author={Ding, C. and Ding, L. and Peng, W.},
p@32: title={Comparison of effects of different learning methods on estimation of distribution algorithms},
p@32: journal={Journal of Software Engineering},
p@32: year={2015},
p@32: volume={9},
p@32: number={3},
p@32: pages={451-468},
p@32: doi={10.3923/jse.2015.451.468},
p@32: note={},
p@32: url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84924609049&partnerID=40&md5=e6419e97e218f8ef1600e3d21e6a9e36},
p@32: document_type={Article},
p@32: source={Scopus},
p@32: }
p@32:
p@32:
p@32: @unpublished{Bengio-et-al-2015-Book,
p@32: title={Deep Learning},
p@32: author={Yoshua Bengio and Ian J. Goodfellow and Aaron Courville},
p@32: note={Book in preparation for MIT Press},
p@32: url={http://www.iro.umontreal.ca/~bengioy/dlbook},
p@32: year={2015}
p@32: }
p@32:
p@32:
p@32: @INPROCEEDINGS{Bertin-Mahieux2011,
p@32: author = {Thierry Bertin-Mahieux and Daniel P.W. Ellis and Brian Whitman and Paul Lamere},
p@32: title = {The Million Song Dataset},
p@32: booktitle = {{Proceedings of the 12th International Conference on Music Information
p@32: Retrieval ({ISMIR} 2011)}},
p@32: year = {2011},
p@32: owner = {thierry},
p@32: timestamp = {2010.03.07}
p@32: }
p@32:
p@32:
p@32: @phdthesis {1242,
p@32: title = {Music Recommendation and Discovery in the Long Tail},
p@32: year = {2008},
p@32: school = {Universitat Pompeu Fabra},
p@32: address = {Barcelona},
p@32: abstract = {
p@32: Music consumption is biased towards a few popular artists. For instance, in 2007 only 1\% of
p@32: all digital tracks accounted for 80\% of all sales. Similarly, 1,000 albums accounted for 50\%
p@32: of all album sales, and 80\% of all albums sold were purchased less than 100 times. There is
p@32: a need to assist people to filter, discover, personalise and recommend from the huge amount
p@32: of music content available along the Long Tail.
p@32:
p@32:
p@32: Current music recommendation algorithms try to
p@32: accurately predict what people demand to listen to. However, quite
p@32: often these algorithms tend to recommend popular -or well-known to the
p@32: user- music, decreasing the effectiveness of the recommendations. These
p@32: approaches focus on improving the accuracy of the recommendations. That
p@32: is, try to make
p@32: accurate predictions about what a user could listen to, or buy next,
p@32: independently of how
p@32: useful to the user could be the provided recommendations.
p@32:
p@32:
p@32: In this Thesis we stress the importance of the user{\textquoteright}s
p@32: perceived quality of the recommendations. We model the Long Tail curve
p@32: of artist popularity to predict -potentially-
p@32: interesting and unknown music, hidden in the tail of the popularity
p@32: curve. Effective recommendation systems should promote novel and
p@32: relevant material (non-obvious recommendations), taken primarily from
p@32: the tail of a popularity distribution.
p@32:
p@32:
p@32: The main contributions of this Thesis are: (i) a novel network-based approach for
p@32: recommender systems, based on the analysis of the item (or user) similarity graph, and the
p@32: popularity of the items, (ii) a user-centric evaluation that measures the user{\textquoteright}s relevance
p@32: and novelty of the recommendations, and (iii) two prototype systems that implement the
p@32: ideas derived from the theoretical work. Our findings have significant implications for
p@32: recommender systems that assist users to explore the Long Tail, digging for content they
p@32: might like.
p@32:
p@32: },
p@32: url = {http://mtg.upf.edu/static/media/PhD_ocelma.pdf},
p@32: author = {Celma, \`{O}.}
p@32: }
p@32:
p@32:
p@32: @inproceedings{gallagher2007bayesian,
p@32: title={Bayesian inference in estimation of distribution algorithms},
p@32: author={Gallagher, Marcus and Wood, Ian and Keith, Jonathan and Sofronov, George},
p@32: booktitle={Evolutionary Computation, 2007. CEC 2007. IEEE Congress on},
p@32: pages={127--133},
p@32: year={2007},
p@32: organization={IEEE}
p@32: }
p@32:
p@32: