Using multidimensional clustering based collaborative filtering approach improving recommendation diversity

Xiaohui Li, Tomohiro Murata

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

16 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationProceedings of the 2012 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology Workshops, WI-IAT 2012
Pages169-174
Number of pages6
DOIs
Publication statusPublished - 2012 Dec 1
Event2012 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology Workshops, WI-IAT 2012 - Macau, China
Duration: 2012 Dec 42012 Dec 7

Publication series

NameProceedings 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
CountryChina
CityMacau
Period12/12/412/12/7

Keywords

  • collaborative filtering
  • multidimensional clustering
  • recommendation diversity
  • recommender systems

ASJC Scopus subject areas

  • Artificial Intelligence
  • Software

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  • Cite this

    Li, X., & Murata, T. (2012). Using multidimensional clustering based collaborative filtering approach improving recommendation diversity. In Proceedings of the 2012 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology Workshops, WI-IAT 2012 (pp. 169-174). [6511671] (Proceedings of the 2012 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology Workshops, WI-IAT 2012). https://doi.org/10.1109/WI-IAT.2012.229