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MusicRoBot: Towards Conversational Context-Aware Music Recommender System

  • Chunyi Zhou
  • Yuanyuan Jin
  • Kai Zhang
  • Jiahao Yuan
  • Shengyuan Li
  • Xiaoling WangEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10828)

Abstract

Traditional recommendation approaches work well on depicting users’ long-term music preference. However, in the conversational applications, it is unable to capture users’ real time music taste, which are dynamic and depend on user context including users’ emotion, current activities or sites. To meet users’ real time music preferences, we have developed a conversational music recommender system based on music knowledge graph, MusicRoBot (Music RecOmmendation Bot). We embed the music recommendation into a chatbot, integrating both the advantages of dialogue system and recommender system. In our system, conversational interaction helps capture more real-time and richer requirements. Users can receive real time recommendation and give feedbacks by conversation. Besides, MusicRoBot also provides the music Q&A function to answer several types of musical question by the music knowledge graph. A WeChat based service has been deployed piloted for volunteers already.

Keywords

Music recommendation Online recommendation Dialogue system Recommender system 

References

  1. 1.
    Christakopoulou, K., Radlinski, F., Hofmann, K.: Towards conversational recommender systems. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 815–824. ACM (2016)Google Scholar
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    Sun, Y., Zhang, Y., Chen, Y., et al.: Conversational recommendation system with unsupervised learning. In: Proceedings of the 10th ACM Conference on Recommender Systems, pp. 397–398. ACM (2016)Google Scholar
  3. 3.
    Qin, L., Chen, S., Zhu, X.: Contextual combinatorial bandit and its application on diversified online recommendation. In: Proceedings of the 2014 SIAM International Conference on Data Mining, pp. 461–469. Society for Industrial and Applied Mathematics (2014)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Chunyi Zhou
    • 1
  • Yuanyuan Jin
    • 1
  • Kai Zhang
    • 2
  • Jiahao Yuan
    • 1
  • Shengyuan Li
    • 1
  • Xiaoling Wang
    • 1
    Email author
  1. 1.Shanghai Key Laboratory of Trustworthy Computing, MOE International Joint Lab of Trustworthy SoftwareEast China Normal UniversityShanghaiChina
  2. 2.Shenzhen Gowild Robotics Co. Ltd.ShenzhenChina

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