Abstract
Nowadays, music data grows rapidly because of the advanced multimedia technology. People are always spending much time to listen to music. This incurs a hot research issue for how to discover the users’ interested music preferences from a large amount of music data. To deal with this issue, the music recommender system has been a solution that can infer the users’ musical interests by a set of learning methods. However, recent music recommender systems encounter problems of new item and data sparsity. To alleviate these problems, in this paper, we propose a new recommender system that fuses user ratings and music low-level features to enhance the recommendation quality. The experimental results show that our proposed recommender system outperforms other well-known music recommender systems.
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Acknowledgements
This research was supported by Ministry of Science and Technology, Taiwan, R.O.C. under grant no. MOST 104-2632-S-424-001 and MOST 104-2221-E-230-019.
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Su, JH., Chiu, TW. (2016). An Item-Based Music Recommender System Using Music Content Similarity. In: Nguyen, N.T., Trawiński, B., Fujita, H., Hong, TP. (eds) Intelligent Information and Database Systems. ACIIDS 2016. Lecture Notes in Computer Science(), vol 9622. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-49390-8_17
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DOI: https://doi.org/10.1007/978-3-662-49390-8_17
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