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2
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40 \bibitem{Lops2011}
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42 \newblock ``Content-based recommender systems: State of the art and trends,''
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43 \newblock in {\em Recommender Systems Handbook}, Francesco Ricci, Lior Rokach,
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44 Bracha Shapira, and Paul~B. Kantor, Eds., pp. 73--105. Springer US, 2011.
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45
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48 \newblock ``An efficient hybrid music recommender system using an incrementally
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52
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53 \bibitem{NIPS2013_5004}
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54 Aaron van~den Oord, Sander Dieleman, and Benjamin Schrauwen,
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56 \newblock in {\em Advances in Neural Information Processing Systems 26}, C.J.C.
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61 Yoshua Bengio, Ian~J. Goodfellow, and Aaron Courville,
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62 \newblock ``Deep learning,''
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63 \newblock Book in preparation for MIT Press, 2015.
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67 \newblock ``The million song dataset,''
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72 Martin Pelikan, Mark~W Hauschild, and Fernando~G Lobo,
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73 \newblock ``Estimation of distribution algorithms,''
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85 Roberto Santana, Concha Bielza, Pedro Larrañaga, Jose~A. Lozano, Carlos
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87 \newblock ``Mateda-2.0: A matlab package for the implementation and analysis of
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88 estimation of distribution algorithms,''
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93 \`{O}. Celma,
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95 \newblock Ph.D. thesis, Universitat Pompeu Fabra, Barcelona, 2008.
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98 G.~Tzanetakis and P.~Cook,
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99 \newblock ``Musical genre classification of audio signals,''
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100 \newblock {\em IEEE Transactions on Speech and Audio Processing}, vol. 10, no.
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102
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103 \bibitem{DBLP:journals/corr/KereliukSL15}
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104 Corey Kereliuk, Bob~L. Sturm, and Jan Larsen,
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105 \newblock ``Deep learning and music adversaries,''
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106 \newblock {\em CoRR}, vol. abs/1507.04761, 2015.
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108 \bibitem{Liang2014781}
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115 \bibitem{gallagher2007bayesian}
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116 Marcus Gallagher, Ian Wood, Jonathan Keith, and George Sofronov,
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120
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121 \end{thebibliography}
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