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p@26: \contentsline {chapter}{\numberline {2}Background}{5}{chapter.2}
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p@26: \contentsline {section}{\numberline {2.1}Online Social Networks}{6}{section.2.1}
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p@26: \contentsline {section}{\numberline {2.2}Music services platforms}{7}{section.2.2}
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p@27: \contentsline {section}{\numberline {2.3}Recommender Systems}{8}{section.2.3}
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p@27: \contentsline {subsubsection}{The cold start problem}{10}{section*.8}
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p@27: \contentsline {subsubsection}{The long tail phenomenon}{11}{section*.9}
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p@27: \contentsline {subsubsection}{Limitations of CB filtering}{12}{section*.11}
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p@29: \contentsline {subsubsection}{Collaborative retrieval music recommender}{16}{section*.12}
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p@29: \contentsline {subsubsection}{Hybrid music recommender}{16}{section*.13}
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p@30: \contentsline {section}{\numberline {2.5}Deep Learning}{18}{section.2.5}
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p@30: \contentsline {subsubsection}{Music Feature Learning}{20}{section*.16}
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p@30: \contentsline {subsubsection}{Deep content-based music recommendation}{22}{section*.18}
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p@30: \contentsline {section}{\numberline {2.6}Estimation of Distribution Algorithms}{23}{section.2.6}
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p@30: \contentsline {subsubsection}{CDNN architecture}{37}{section*.24}
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p@30: \contentsline {subsubsection}{Modelling with Permutation EDA}{40}{section*.28}
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p@30: \contentsline {subsubsection}{Modelling with $UMDA_c^G$}{40}{section*.29}
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p@30: \contentsline {subsection}{\numberline {3.3.3}Top-N songs recommendation}{41}{subsection.3.3.3}
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p@30: \contentsline {subsubsection}{Top-N recommendations in CB baseline}{42}{section*.30}
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p@30: \contentsline {subsubsection}{Top-N recommendations in hybrid music recommender}{43}{section*.31}
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p@30: \contentsline {section}{\numberline {3.4}Summary}{43}{section.3.4}
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p@30: \contentsline {chapter}{\numberline {4}Experiments}{44}{chapter.4}
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p@30: \contentsline {section}{\numberline {5.1}Genre classification results}{48}{section.5.1}
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p@30: \contentsline {section}{\numberline {6.1}Future work}{52}{section.6.1}
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p@30: \contentsline {chapter}{References}{53}{section.6.1}