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- W2592471037 abstract "Online music radios, such as Last.fm and Douban.fm, which provide users with free music, have gained much popularity in recent years. In online music radios, music recommendation plays a central role in recommending the most relevant music to users who are most likely to listen to. Different from traditional on-demand music service, online music radios have only users' listening records instead of users' ratings, which make it difficult to give an accurate recommendation. In this paper, we use a pairwise ranking based collaborative filtering method which performs well on one-class collaborative filtering. We future extend our model by considering users' artist preference, which based on the assumption that users who have listened tracks belonging to the same artist are likely to have similar preferences. The resulting model is able to generalize considerably better for users with few ratings. Experimental results on real-world data sets show the performance of our method." @default.
- W2592471037 created "2017-03-16" @default.
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- W2592471037 date "2016-06-01" @default.
- W2592471037 modified "2023-10-16" @default.
- W2592471037 title "Ranking-Based Music Recommendation in Online Music Radios" @default.
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- W2592471037 doi "https://doi.org/10.1109/dsc.2016.102" @default.
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