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

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

7 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationProceedings of the 8th International Conference on Music Information Retrieval, ISMIR 2007
Pages89-94
Number of pages6
Publication statusPublished - 2007 Dec 1
Event8th International Conference on Music Information Retrieval, ISMIR 2007 - Vienna, Austria
Duration: 2007 Sep 232007 Sep 27

Publication series

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

Conference

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

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ASJC Scopus subject areas

  • Music
  • Information Systems

Cite this

Yoshii, K., Goto, M., Komatani, K., Ogata, T., & Okuno, H. G. (2007). Improving efficiency and scalability of model-based music recommender system based on incremental training. In Proceedings of the 8th International Conference on Music Information Retrieval, ISMIR 2007 (pp. 89-94). (Proceedings of the 8th International Conference on Music Information Retrieval, ISMIR 2007).