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

研究成果: Conference contribution

91 被引用数 (Scopus)

抄録

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.

本文言語English
ホスト出版物のタイトルISMIR 2006 - 7th International Conference on Music Information Retrieval
ページ296-301
ページ数6
出版ステータスPublished - 2006 12 1
外部発表はい
イベント7th International Conference on Music Information Retrieval, ISMIR 2006 - Victoria, BC, Canada
継続期間: 2006 10 82006 10 12

出版物シリーズ

名前ISMIR 2006 - 7th International Conference on Music Information Retrieval

Conference

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

ASJC Scopus subject areas

  • 音楽
  • 情報システム

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