To Have a Tiger by the Tail: Improving Music Recommendation for International Users
نویسنده
چکیده
One of the most useful signals for building a music similarity representation for recommendation comes from learning a collaborative filtering (CF) space from user data such as user listening behavior and user ratings. However, the distribution of this data is heavily biased towards the most popular music, what we call the head of the distribution. We have much less data on less popular artists from the tail of the distribution. In consequence, the CF space represents relatively accurately head artists, and becomes noisy for tail artists.
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