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• W2048657872 abstract "Existing content-based music recommendation systems typically employ a textit{two-stage} approach. They first extract traditional audio content features such as Mel-frequency cepstral coefficients and then predict user preferences. However, these traditional features, originally not created for music recommendation, cannot capture all relevant information in the audio and thus put a cap on recommendation performance. Using a novel model based on deep belief network and probabilistic graphical model, we unify the two stages into an automated process that simultaneously learns features from audio content and makes personalized recommendations. Compared with existing deep learning based models, our model outperforms them in both the warm-start and cold-start stages without relying on collaborative filtering (CF). We then present an efficient hybrid method to seamlessly integrate the automatically learnt features and CF. Our hybrid method not only significantly improves the performance of CF but also outperforms the traditional feature mbased hybrid method." @default.
• W2048657872 created "2016-06-24" @default.
• W2048657872 creator A5065592637 @default.
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• W2048657872 date "2014-11-03" @default.
• W2048657872 modified "2023-10-16" @default.
• W2048657872 title "Improving Content-based and Hybrid Music Recommendation using Deep Learning" @default.
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• W2048657872 doi "https://doi.org/10.1145/2647868.2654940" @default.
• W2048657872 hasPublicationYear "2014" @default.
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