Using multidimensional clustering based collaborative filtering approach improving recommendation diversity

Xiaohui Li, Tomohiro Murata

研究成果: Conference contribution

19 被引用数 (Scopus)

抄録

In this paper, we present a hybrid recommendation approach for discovering potential preferences of individual users. The proposed approach provides a flexible solution that incorporates multidimensional clustering into a collaborative filtering recommendation model to provide a quality recommendation. This facilitates to obtain user clusters which have diverse preference from multi-view for improving effectiveness and diversity of recommendation. The presented algorithm works in three phases: data preprocessing and multidimensional clustering, choosing the appropriate clusters and recommending for the target user. The performance of proposed approach is evaluated using a public movie dataset and compared with two representative recommendation algorithms. The empirical results demonstrate that our proposed approach is likely to trade-off on increasing the diversity of recommendations while maintaining the accuracy of recommendations.

本文言語English
ホスト出版物のタイトルProceedings of the 2012 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology Workshops, WI-IAT 2012
ページ169-174
ページ数6
DOI
出版ステータスPublished - 2012 12 1
イベント2012 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology Workshops, WI-IAT 2012 - Macau, China
継続期間: 2012 12 42012 12 7

出版物シリーズ

名前Proceedings of the 2012 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology Workshops, WI-IAT 2012

Conference

Conference2012 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology Workshops, WI-IAT 2012
国/地域China
CityMacau
Period12/12/412/12/7

ASJC Scopus subject areas

  • 人工知能
  • ソフトウェア

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