Hybrid collaborative and content-based music recommendation using probabilistic model with latent user preferences

Kazuyoshi Yoshii, Masataka Goto, Kazunori Komatani, Tetsuya Ogata, Hiroshi G. Okuno

Research output: Chapter in Book/Report/Conference proceedingConference contribution

86 Citations (Scopus)

Abstract

This paper presents a hybrid music recommendation method that solves problems of two prominent conventional methods: collaborative filtering and content-based recommendation. The former cannot recommend musical pieces that have no ratings because recommendations are based on actual user ratings. In addition, artist variety in recommended pieces tends to be poor. The latter, which recommends musical pieces that are similar to users' favorites in terms of music content, has not been fully investigated. This induces unreliability in modeling of user preferences; the content similarity does not completely reflect the preferences. Our method integrates both rating and content data by using a Bayesian network called an aspect model. Unobservable user preferences are directly represented by introducing latent variables, which are statistically estimated. To verify our method, we conducted experiments by using actual audio signals of Japanese songs and the corresponding rating data collected from Amazon. The results showed that our method outperforms the two conventional methods in terms of recommendation accuracy and artist variety and can reasonably recommend pieces even if they have no ratings.

Original languageEnglish
Title of host publicationISMIR 2006 - 7th International Conference on Music Information Retrieval
Pages296-301
Number of pages6
Publication statusPublished - 2006 Dec 1
Externally publishedYes
Event7th International Conference on Music Information Retrieval, ISMIR 2006 - Victoria, BC, Canada
Duration: 2006 Oct 82006 Oct 12

Publication series

NameISMIR 2006 - 7th International Conference on Music Information Retrieval

Conference

Conference7th International Conference on Music Information Retrieval, ISMIR 2006
CountryCanada
CityVictoria, BC
Period06/10/806/10/12

Keywords

  • Collaborative filtering
  • Content-based recommendation
  • Hybrid method
  • Probabilistic model

ASJC Scopus subject areas

  • Music
  • Information Systems

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  • Cite this

    Yoshii, K., Goto, M., Komatani, K., Ogata, T., & Okuno, H. G. (2006). Hybrid collaborative and content-based music recommendation using probabilistic model with latent user preferences. In ISMIR 2006 - 7th International Conference on Music Information Retrieval (pp. 296-301). (ISMIR 2006 - 7th International Conference on Music Information Retrieval).