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      Learning Audio Embeddings with User Listening Data for Content-based Music Recommendation

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          Abstract

          Personalized recommendation on new track releases has always been a challenging problem in the music industry. To combat this problem, we first explore user listening history and demographics to construct a user embedding representing the user's music preference. With the user embedding and audio data from user's liked and disliked tracks, an audio embedding can be obtained for each track using metric learning with Siamese networks. For a new track, we can decide the best group of users to recommend by computing the similarity between the track's audio embedding and different user embeddings, respectively. The proposed system yields state-of-the-art performance on content-based music recommendation tested with millions of users and tracks. Also, we extract audio embeddings as features for music genre classification tasks. The results show the generalization ability of our audio embeddings.

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          Author and article information

          Journal
          29 October 2020
          Article
          2010.15389
          a8ade129-29ae-4295-a67e-f301dd5a9081

          http://creativecommons.org/licenses/by-nc-sa/4.0/

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          Custom metadata
          cs.SD eess.AS

          Electrical engineering,Graphics & Multimedia design
          Electrical engineering, Graphics & Multimedia design

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