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Move 7Digital dataset to Downloads

@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@unpublished{Bengio-et-al-2015-Book,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 = {<p class="small">
	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.
	</p>
	<p class="small">
	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.
	</p>
	<p class="small">
	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.
	</p>
	<p class="small">
	The main contributions of this Thesis are: <em>(i)</em> 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, <em>(ii)</em> a user-centric evaluation that measures the user{\textquoteright}s relevance
	and novelty of the recommendations, and <em>(iii)</em> 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.
	</p>
	},
	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}
}