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- W2056246507 abstract "Online music streaming services (MSS) experienced exponential growth over the past decade. The giant MSS providers not only built massive music collection with metadata, they also accumulated large amount of heterogeneous data generated from users, e.g. listening history, comment, bookmark, and user generated playlist. While various kinds of user data can potentially be used to enhance the music recommendation performance, most existing studies only focused on audio content features and collaborative filtering approaches based on simple user listening history or music rating. In this paper, we propose a novel approach to solve the music recommendation problem by means of heterogeneous graph mining. Meta-path based features are automatically generated from a content-rich heterogeneous graph schema with 6 types of nodes and 16 types of relations. Meanwhile, we use learning-to-rank approach to integrate different features for music recommendation. Experiment results show that the automatically generated graphical features significantly (p<0.0001) enhance state-of-the-art collaborative filtering algorithm." @default.
- W2056246507 created "2016-06-24" @default.
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- W2056246507 date "2015-08-09" @default.
- W2056246507 modified "2023-09-25" @default.
- W2056246507 title "Automatic Feature Generation on Heterogeneous Graph for Music Recommendation" @default.
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- W2056246507 doi "https://doi.org/10.1145/2766462.2767808" @default.
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