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.
X. Wang—This work was supported by NSFC grants (No. 61472141), Shanghai Knowledge Service Platform Project (No. ZF1213) SHEITC and Shanghai Agriculture Applied Technology Development Program (No. G20160201).
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Notes
- 1.
xiami’s homepage: http://www.xiami.com/.
- 2.
neo4j’s homepage: https://neo4j.com/.
- 3.
emotibot’s homepage: http://www.emotibot.com.
- 4.
Gowild’s homepage: http://www.gowild.cn.
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Zhou, C., Jin, Y., Zhang, K., Yuan, J., Li, S., Wang, X. (2018). MusicRoBot: Towards Conversational Context-Aware Music Recommender System. In: Pei, J., Manolopoulos, Y., Sadiq, S., Li, J. (eds) Database Systems for Advanced Applications. DASFAA 2018. Lecture Notes in Computer Science(), vol 10828. Springer, Cham. https://doi.org/10.1007/978-3-319-91458-9_55
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DOI: https://doi.org/10.1007/978-3-319-91458-9_55
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