Improving music recommendation using distributed representation

Dongjing WANG, Shuiguang DENG, Songguo LIU, Guandong XU

Research output: Chapter in Book/Report/Conference proceedingChapters

14 Citations (Scopus)

Abstract

In this paper, a music recommendation approach based on distributed representation is presented. The proposed approach firstly learns the distributed representations of music pieces and acquires users' preferences from listening records. Then, it recommends appropriate music pieces whose distributed representations are in accordance with target users' preferences. Experiments on a real world dataset demonstrate that the proposed approach outperforms the state-of-the-art methods. Copyright © 2016 owner/author(s).

Original languageEnglish
Title of host publicationProceedings of the 25th International Conference on World Wide Web
Place of PublicationSwitzerland
PublisherInternational World Wide Web Conferences Steering Committee
Pages125-126
ISBN (Electronic)9781450341448
DOIs
Publication statusPublished - Apr 2016

Citation

Wang, D., Deng, S., Liu, S., & Xu, G. (2016). Improving music recommendation using distributed representation. In Proceedings of the 25th International Conference on World Wide Web (pp. 125-126). International World Wide Web Conferences Steering Committee. https://doi.org/10.1145/2872518.2889399

Keywords

  • Music recommendation
  • Distributed representation

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