Music recommendation using graph based quality model
Signal Processing(2016)
摘要
Nowadays, listening to music has become a habit for almost everyone. Music recommendation helps the users discover the songs they like to listen. In this paper, we propose a music recommendation framework based on graph based quality model to make fine-grained music recommendation. We first discover the recommendation cues, which we called user's preference relations from the users' ratings. Then we model them using the quality model and propose a regularization framework to calculate the recommendation probability of songs. Our experiments show that the proposed framework is superior to two traditional algorithms, especially in solving the cold start problem in recommendation. HighlightsWe propose a fine-grained song recommendation framework.We propose a song preference relation discovering.We propose a preference graph to model the discovered preference.We prove that the framework is better than traditional song recommendation algorithms.
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关键词
Music recommendation,Quality model,Graph based ranking
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