annotate Report/references.bib @ 28:a95e656907c3

Updated report

@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}
}
@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={cited By 0},
	url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84924609049&partnerID=40&md5=e6419e97e218f8ef1600e3d21e6a9e36},
	document_type={Article},
	source={Scopus},
}

@ARTICLE{Jin2014113,
	author={Jin, C. and Jin, S.-W.},
	title={Software reliability prediction model based on support vector regression with improved estimation of distribution algorithms},
	journal={Applied Soft Computing Journal},
	year={2014},
	volume={15},
	pages={113-120},
	doi={10.1016/j.asoc.2013.10.016},
	note={cited By 0},
	url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84889065631&partnerID=40&md5=6ba595eee679fa8355329646504b3ae3},
	document_type={Article},
	source={Scopus},
}
@online{1_ufldlstanfordedu_2015,
	author={Ufldl.stanford.edu,},
	title={Unsupervised Feature Learning and Deep Learning Tutorial},
	url={http://ufldl.stanford.edu/tutorial/supervised/ConvolutionalNeuralNetwork/},
	urldate={2015-8-30},
	year={2015}
}
@online{1_lecun_2015,
	author={LeCun, Yann},
	title={MNIST Demos on Yann LeCun's website},
	url={http://yann.lecun.com/exdb/lenet/},
	urldate={2015-8-30},
	journal={Yann.lecun.com},
	year={2015}
}
@online{1_brown_2014,
	author={Brown, Larry},
	title={Accelerate Machine Learning with the cuDNN Deep Neural Network Library},
	url={http://devblogs.nvidia.com/parallelforall/accelerate-machine-learning-cudnn-deep-neural-network-library/},
	urldate={2015-8-30},
	journal={Parallel Forall},
	year={2014}
}

@article{hinton2012deep,
	title={Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups},
	author={Hinton, Geoffrey and Deng, Li and Yu, Dong and Dahl, George E and Mohamed, Abdel-rahman and Jaitly, Navdeep and Senior, Andrew and Vanhoucke, Vincent and Nguyen, Patrick and Sainath, Tara N and others},
	journal={Signal Processing Magazine, IEEE},
	volume={29},
	number={6},
	pages={82--97},
	year={2012},
	publisher={IEEE}
}
@article{weston2012latent,
	title={Latent collaborative retrieval},
	author={Weston, Jason and Wang, Chong and Weiss, Ron and Berenzweig, Adam},
	journal={arXiv preprint arXiv:1206.4603},
	year={2012}
}

@MISC{Bastien-Theano-2012,
	author = {Bastien, Fr{\'{e}}d{\'{e}}ric and Lamblin, Pascal and Pascanu, Razvan and Bergstra, James and Goodfellow, Ian J. and Bergeron, Arnaud and Bouchard, Nicolas and Bengio, Yoshua},
	title = {Theano: new features and speed improvements},
	year = {2012},
	howpublished = {Deep Learning and Unsupervised Feature Learning NIPS 2012 Workshop},
	abstract = {Theano is a linear algebra compiler that optimizes a user’s symbolically-specified
	mathematical computations to produce efficient low-level implementations. In
	this paper, we present new features and efficiency improvements to Theano, and
	benchmarks demonstrating Theano’s performance relative to Torch7, a recently
	introduced machine learning library, and to RNNLM, a C++ library targeted at
	recurrent neural networks.}
}

@INPROCEEDINGS{bergstra+al:2010-scipy,
	author = {Bergstra, James and Breuleux, Olivier and Bastien, Fr{\'{e}}d{\'{e}}ric and Lamblin, Pascal and Pascanu, Razvan and Desjardins, Guillaume and Turian, Joseph and Warde-Farley, David and Bengio, Yoshua},
	month = jun,
	title = {Theano: a {CPU} and {GPU} Math Expression Compiler},
	booktitle = {Proceedings of the Python for Scientific Computing Conference ({SciPy})},
	year = {2010},
	location = {Austin, TX},
	note = {Oral Presentation},
	abstract = {Theano is a compiler for mathematical expressions in Python that combines the convenience of NumPy’s syntax with the speed of optimized native machine language. The user composes mathematical expressions in a high-level description that mimics NumPy’s syntax and semantics, while being statically typed and
	functional (as opposed to imperative). These expressions allow Theano to provide symbolic differentiation. Before performing computation, Theano optimizes the choice of expressions, translates
	them into C++ (or CUDA for GPU), compiles them into dynamically loaded Python modules, all automatically. Common machine learning algorithms implemented with Theano are from 1.6× to 7.5× faster than competitive alternatives (including those implemented with C/C++, NumPy/SciPy and MATLAB) when compiled for the
	CPU and between 6.5× and 44× faster when compiled for the GPU. This paper illustrates how to use Theano, outlines the scope of the compiler, provides benchmarks on both CPU and GPU processors, and explains its overall design.}
}

@article{kereliuk15,
	title={Deep Learning and Music Adversaries},
	author={Kereliuk, Corey and Sturm, Bob L and Larsen, Jan},
	journal={arXiv preprint arXiv:1507.04761},
	year={2015}
}

@article{DBLP:journals/corr/abs-1305-1114,
	author    = {Djallel Bouneffouf},
	title     = {Towards User Profile Modelling in Recommender System},
	journal   = {CoRR},
	volume    = {abs/1305.1114},
	year      = {2013},
	url       = {http://arxiv.org/abs/1305.1114},
	timestamp = {Sun, 02 Jun 2013 20:48:21 +0200},
	biburl    = {http://dblp.uni-trier.de/rec/bib/journals/corr/abs-1305-1114},
	bibsource = {dblp computer science bibliography, http://dblp.org}
}

@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},
}
@online{1_blogseagatesoftcom_2015,
	author={Blog.seagatesoft.com,},
	title={Belajar Sistem Perekomendasi «  Corat-coret di Halaman Web},
	url={http://blog.seagatesoft.com/2013/07/14/belajar-sistem-perekomendasi/},
	urldate={2015-8-29},
	year={2015}
}

@inproceedings{sarwar2001item,
	title={Item-based collaborative filtering recommendation algorithms},
	author={Sarwar, Badrul and Karypis, George and Konstan, Joseph and Riedl, John},
	booktitle={Proceedings of the 10th international conference on World Wide Web},
	pages={285--295},
	year={2001},
	organization={ACM}
}

@CONFERENCE{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},
}

@online{2_the_economist_2005,
	author={The Economist,},
	title={United we find},
	url={http://www.economist.com/node/3714044},
	urldate={2015-8-28},
	year={2005}
}

@online{1_siddharths_blog_2013,
	author={},
	title={Recommendation Engine},
	url={https://spatnaik77.wordpress.com/2013/07/17/recommendation-engine/},
	urldate={2015-8-28},
	year={2013}
}

@online{1_deeplearning.net_2015,
	author={Deeplearning.net,},
	title={Convolutional Neural Networks (LeNet) — DeepLearning 0.1 documentation},
	url={http://deeplearning.net/tutorial/lenet.html},
	urldate={2015-8-28},
	year={2015}
}
@ARTICLE{Casey2008668,
	author={Casey, M.A. and Veltkamp, R. and Goto, M. and Leman, M. and Rhodes, C. and Slaney, M.},
	title={Content-based music information retrieval: Current directions and future challenges},
	journal={Proceedings of the IEEE},
	year={2008},
	volume={96},
	number={4},
	pages={668-696},
	doi={10.1109/JPROC.2008.916370},
	art_number={4472077},
	note={},
	url={http://www.scopus.com/inward/record.url?eid=2-s2.0-64649105397&partnerID=40&md5=2d8ec7231e10bc686566dd419ce47ae8},
	document_type={Article},
	source={Scopus},
}

@CONFERENCE{Celma2006,
	author={Celma, O. and Herrera, P. and Serra, X.},
	title={Bridging the music semantic gap},
	journal={CEUR Workshop Proceedings},
	year={2006},
	volume={187},
	page_count={12},
	note={},
	url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84884332226&partnerID=40&md5=d028cb2aca5d2d6d8f25a8a8b555edbf},
	document_type={Conference Paper},
	source={Scopus},
}

@online{1_spotify_press_2014,
	author={},
	title={Spotify Acquires The Echo Nest},
	url={https://press.spotify.com/us/2014/03/06/spotify-acquires-the-echo-nest/},
	urldate={2015-8-27},
	year={2014}
}
@article{smith2009social,
	title={The social media revolution},
	author={Smith, Tom},
	journal={International journal of market research},
	volume={51},
	number={4},
	pages={559--561},
	year={2009}
}
@article{Putzke2014519,
	title = "Cross-cultural gender differences in the adoption and usage of social media platforms – An exploratory study of Last.FM ",
	journal = "Computer Networks ",
	volume = "75, Part B",
	number = "",
	pages = "519 - 530",
	year = "2014",
	note = "Special Issue on Online Social NetworksThe Connectedness, Pervasiveness and Ubiquity of Online Social Networks ",
	issn = "1389-1286",
	doi = "http://dx.doi.org/10.1016/j.comnet.2014.08.027",
	url = "http://www.sciencedirect.com/science/article/pii/S1389128614003302",
	author = "Johannes Putzke and Kai Fischbach and Detlef Schoder and Peter A. Gloor",
	keywords = "Adoption",
	keywords = "Cross-cultural differences",
	keywords = "Gender",
	keywords = "Social media ",
	abstract = "Abstract This paper examines cross-cultural gender differences in the adoption and usage of the social media platform Last.FM. From a large-scale empirical study of 3748 Last.FM users from Australia, Finland, Germany, and the United States of America, we find: (1) men listen to more pieces of music on social media platforms than do women; (2) women focus their listening on fewer musical genres and fewer tracks than do men; (3) women register on Last.FM later than do “early adopting” men (absolutely and in comparison to their friends), but at a younger age; (4) women maintain more virtual friendships on Last.FM than do men; and (5) women, when choosing music to listen to on social media platforms, are more likely than are men to choose tracks that correspond to mainstream tastes. "
}

@CONFERENCE{Park200811,
	author={Park, Y.-J. and Tuzhilin, A.},
	title={The long tail of recommender systems and how to leverage it},
	journal={RecSys'08: Proceedings of the 2008 ACM Conference on Recommender Systems},
	year={2008},
	pages={11-18},
	doi={10.1145/1454008.1454012},
	note={},
	url={http://www.scopus.com/inward/record.url?eid=2-s2.0-63449136183&partnerID=40&md5=648e50cac2d99764f891b5bc4b97bbfe},
	document_type={Conference Paper},
	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},
}

@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},
}

@online{1_hypebot.com_2015,
	author={Hypebot.com,},
	title={Streaming Music Discovery: It's More Than Just Showing Album Credits - hypebot},
	url={http://www.hypebot.com/hypebot/2015/07/streaming-music-discovery-its-more-than-just-showing-album-credits.html},
	urldate={2015-8-26},
	year={2015}
}

@online{ringen_2015,
	author={Ringen, Jonathan},
	title={Spotify, Apple Music, And The Streaming Wars: 5 Things We've Learned},
	url={http://www.fastcompany.com/3048653/innovation-agents/listen-up},
	urldate={2015-8-26},
	journal={Fast Company},
	year={2015}
}

@online{recsys2012,
	author = {},
	title = {Recommender Systems},
	year = {2012}, 
	url = {http://recommender-systems.org/},
	note = {[Accessed: 26th August 2015]}
}

@article{Hejazi15,
	author={Hejazi,S. A. and Stapleton,S. P.},
	year={2015},
	title={A self-organized network for load balancing using intelligent distributed antenna system},
	journal={Canadian Journal of Electrical and Computer Engineering},
	volume={38},
	number={2},
	pages={89-99},
	url={www.scopus.com},
}


@book{larranaga2002estimation,
	title={Estimation of distribution algorithms: A new tool for evolutionary computation},
	author={Larranaga, Pedro and Lozano, Jose A},
	volume={2},
	year={2002},
	publisher={Springer Science \& Business Media}
}

@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}
}

@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{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={},
	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={},
	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={},
	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={},
	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={},
	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={},
	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}
}