Conversational music recommendation based on bandits

Chunyi Zhou, Yuanyuan Jin, Xiaoling Wang, Yingjie Zhang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

12 Scopus citations

Abstract

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.

Original languageEnglish
Title of host publicationProceedings - 11th IEEE International Conference on Knowledge Graph, ICKG 2020
EditorsEnhong Chen, Grigoris Antoniou, Xindong Wu, Vipin Kumar
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages41-48
Number of pages8
ISBN (Electronic)9781728181561
DOIs
StatePublished - Aug 2020
Event11th IEEE International Conference on Knowledge Graph, ICKG 2020 - Virtual, Online, China
Duration: 9 Aug 202011 Aug 2020

Publication series

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

Conference

Conference11th IEEE International Conference on Knowledge Graph, ICKG 2020
Country/TerritoryChina
CityVirtual, Online
Period9/08/2011/08/20

Keywords

  • Dialog system
  • Knowledge graph
  • Online recommendation
  • Recommender system

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