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@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}
}

@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={cited By 0},
	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={cited By 0},
	url={http://www.scopus.com/inward/record.url?eid=2-s2.0-39649112098\&partnerID=40\&md5=6827f82844ae1da58a6fa95caf5092d9},
	document_type={Article},
	source={Scopus},
}


@article{JCC4:JCC4393,
author = {boyd, danah m. and Ellison, Nicole B.},
title = {Social Network Sites: Definition, History, and Scholarship},
journal = {Journal of Computer-Mediated Communication},
volume = {13},
number = {1},
publisher = {Blackwell Publishing Inc},
issn = {1083-6101},
url = {http://dx.doi.org/10.1111/j.1083-6101.2007.00393.x},
doi = {10.1111/j.1083-6101.2007.00393.x},
pages = {210--230},
year = {2007},
}

@ARTICLE{Castanedo2013,
	author={Castanedo, F.},
	title={A review of data fusion techniques},
	journal={The Scientific World Journal},
	year={2013},
	volume={2013},
	doi={10.1155/2013/704504},
	art_number={704504},
	note={cited By 0},
	url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84888882639\&partnerID=40\&md5=827fabc750db24f662fdae1c798f2507},
	document_type={Review},
	source={Scopus},
}


@CONFERENCE{Lee20091096,
	author={Lee, H. and Yan, L. and Pham, P. and Ng, A.Y.},
	title={Unsupervised feature learning for audio classification using convolutional deep belief networks},
	journal={Advances in Neural Information Processing Systems 22 - Proceedings of the 2009 Conference},
	year={2009},
	pages={1096-1104},
	note={cited By 0},
	url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84863380535\&partnerID=40\&md5=e872a6227c816850167f91bb2d41d8b7},
	document_type={Conference Paper},
	source={Scopus},
}


@TechReport {export:115396,
abstract     = {<p>Recommender systems are now popular both commercially and in the research
                community, where many approaches have been suggested for providing
                recommendations. In many cases a system designer that wishes to employ a
                recommendation system must choose between a set of candidate approaches.  A first
                step towards selecting an appropriate algorithm is to decide which properties of
                the application to focus upon when making this choice.  Indeed, recommendation
                systems have a variety of properties that may affect user experience, such as
                accuracy, robustness, scalability, and so forth. In this paper we discuss how to
                compare recommenders based on a set of properties that are relevant for e
                application. We focus on comparative studies, where a few algorithms are compared
                using some evaluation metric, rather than absolute benchmarking of algorithms. We
                describe experimental settings appropriate for making choices between algorithms.
                We review three types of experiments, starting with an offline setting, where
                recommendation approaches are compared without user interaction, then reviewing
                user studies, where a small group of subjects experiment with the system and
                report on the experience, and finally describe large scale online experiments,
                where real user populations interact with the system. In each of these cases we
                describe types of questions that can be answered, and suggest protocols for
                experimentation. We also discuss how to draw trustworthy conclusions from e
                conducted experiments. We then review a large set of properties, and explain how
                to evaluate systems given relevant properties. We also survey a large set of
                evaluation metrics in the context of the property that they evaluate.</p>},
author       = {Guy Shani and Asela Gunawardana},
month        = {November},
number       = {MSR-TR-2009-159},
publisher    = {Microsoft Research},
title        = {Evaluating Recommender Systems},
url          = {http://research.microsoft.com/apps/pubs/default.aspx?id=115396},
year         = {2009},
}

@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{pachet2001musical,
	title={Musical data mining for electronic music distribution},
	author={Pachet, Fran{\c{c}}ois and Westermann, Gert and Laigre, Damien},
	booktitle={Web Delivering of Music, 2001. Proceedings. First International Conference on},
	pages={101--106},
	year={2001},
	organization={IEEE}
}

@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={cited By 976},
	url={http://www.scopus.com/inward/record.url?eid=2-s2.0-0036648502\&partnerID=40\&md5=72d2fee186b42c9998f13415cbb79eea},
	document_type={Article},
	source={Scopus},
}

@CONFERENCE{Sturm20127,
	author={Sturm, B.L.},
	title={An analysis of the GTZAN music genre dataset},
	journal={MIRUM 2012 - Proceedings of the 2nd International ACM Workshop on Music Information Retrieval with User-Centered and Multimodal Strategies, Co-located with ACM Multimedia 2012},
	year={2012},
	pages={7-12},
	doi={10.1145/2390848.2390851},
	note={cited By 0},
	url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84870497334\&partnerID=40\&md5=40a48c1c9d787308dd315694b54b64ec},
	document_type={Conference Paper},
	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}
}

@CONFERENCE{Sigtia20146959,
	author={Sigtia, S. and Dixon, S.},
	title={Improved music feature learning with deep neural networks},
	journal={ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings},
	year={2014},
	pages={6959-6963},
	doi={10.1109/ICASSP.2014.6854949},
	art_number={6854949},
	note={cited By 0},
	url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84905259152\&partnerID=40\&md5=3441dfa8c7998a8eb39f668d43efb8a1},
	document_type={Conference Paper},
	source={Scopus},
}

@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{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={cited By 0},
	url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84892865461\&partnerID=40\&md5=a2927d36b493e8ef4d1cdab3055fa68b},
	document_type={Article},
	source={Scopus},
}

@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}
}

@inproceedings{zhang2014deep,
	title={A deep representation for invariance and music classification},
	author={Zhang, Chiyuan and Evangelopoulos, Georgios and Voinea, Stephen and Rosasco, Lorenzo and Poggio, Tomaso},
	booktitle={Acoustics, Speech and Signal Processing (ICASSP), 2014 IEEE International Conference on},
	pages={6984--6988},
	year={2014},
	organization={IEEE}
}