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Conversational music recommendation based on bandits

  • Chunyi Zhou
  • , Yuanyuan Jin
  • , Xiaoling Wang*
  • , Yingjie Zhang
  • *此作品的通讯作者
  • East China Normal University
  • Tongji University
  • Monitoring and Research Center

科研成果: 书/报告/会议事项章节会议稿件同行评审

摘要

Music is one of the most popular products in the recommender system, and there have been many various methods of exploring music recommendations. Traditional music recommendations commonly collect users' feedbacks in limited ways for preference analysis. The text dialogue is a direct and natural interactive mode, providing diversified information. In this paper, we discuss the music recommendation in an innovative scenario - a conversational music recommendation model, which integrates the advantages both from the recommender system and dialog system. This paper adopts a 'user ask, system respond' interactive way to obtain users' real-time requirements, and users are allowed to express their requirements on music in free text. In order to face the fast-changing music preferences, this paper adopts the bandit-based algorithm to absorb users' attitudes to the current recommendation, and the results show these methods achieve better performance than baselines. Besides, it also constructs a music-domain knowledge graph to support the richer users' musical expressions with millions of music items and tens of millions of relations.

源语言英语
主期刊名Proceedings - 11th IEEE International Conference on Knowledge Graph, ICKG 2020
编辑Enhong Chen, Grigoris Antoniou, Xindong Wu, Vipin Kumar
出版商Institute of Electrical and Electronics Engineers Inc.
41-48
页数8
ISBN(电子版)9781728181561
DOI
出版状态已出版 - 8月 2020
活动11th IEEE International Conference on Knowledge Graph, ICKG 2020 - Virtual, Online, 中国
期限: 9 8月 202011 8月 2020

出版系列

姓名Proceedings - 11th IEEE International Conference on Knowledge Graph, ICKG 2020

会议

会议11th IEEE International Conference on Knowledge Graph, ICKG 2020
国家/地区中国
Virtual, Online
时期9/08/2011/08/20

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