Abstract
Music recommendation attracts great attention for music providers to improve their services as the volume of new music increases quickly. It is a great challenge for users to find their interested songs from such a large size of collections. In the previous studies, common strategies can be categorized into content-based music recommendation and collaborative music filtering. Content-based recommendation systems predict users’ preferences in terms of the music content. Collaborative filtering systems predict users’ ratings based on the preferences of the friends of the targeting user. In this study, we proposed a hybrid approach to provide personalized music recommendations. This is achieved by extracting audio features of songs and integrating these features and user personalities for context-aware recommendation using the state-of-the-art support vector machines (SVM). Our experiments show the effectiveness of this proposed approach for personalized music recommendation.
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Cheng, R., Tang, B. (2016). A Music Recommendation System Based on Acoustic Features and User Personalities. In: Cao, H., Li, J., Wang, R. (eds) Trends and Applications in Knowledge Discovery and Data Mining. PAKDD 2016. Lecture Notes in Computer Science(), vol 9794. Springer, Cham. https://doi.org/10.1007/978-3-319-42996-0_17
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DOI: https://doi.org/10.1007/978-3-319-42996-0_17
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