Improving efficiency and scalability of model-based music recommender system based on incremental training

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

*この研究の対応する著者

研究成果

8 被引用数 (Scopus)

抄録

We aimed at improving the efficiency and scalability of a hybrid music recommender system based on a probabilistic generative model that integrates both collaborative data (rating scores provided by users) and content-based data (acoustic features of musical pieces). Although the hybrid system was proved to make accurate recommendations, it lacks efficiency and scalability. In other words, the entire model needs to be re-trained from scratch whenever a new score, user, or piece is added. Furthermore, the system cannot deal with practical numbers of users and pieces on an enterprise scale. To improve efficiency, we propose an incremental method that partially updates the model at low computational cost. To enhance scalability, we propose a method that first constructs a small "core" model over fewer virtual representatives created from real users and pieces, and then adds the real users and pieces to the core model by using the incremental method. The experimental results revealed that the proposed system was not only efficient and scalable but also outperformed the original system in terms of accuracy.

本文言語English
ホスト出版物のタイトルProceedings of the 8th International Conference on Music Information Retrieval, ISMIR 2007
ページ89-94
ページ数6
出版ステータスPublished - 2007 12 1
外部発表はい
イベント8th International Conference on Music Information Retrieval, ISMIR 2007 - Vienna, Austria
継続期間: 2007 9 232007 9 27

出版物シリーズ

名前Proceedings of the 8th International Conference on Music Information Retrieval, ISMIR 2007

Conference

Conference8th International Conference on Music Information Retrieval, ISMIR 2007
国/地域Austria
CityVienna
Period07/9/2307/9/27

ASJC Scopus subject areas

  • 音楽
  • 情報システム

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