A method for hybrid personalized recommender based on clustering of fuzzy user profiles

Shan Xu, Junzo Watada

    研究成果: Conference contribution

    5 被引用数 (Scopus)

    抄録

    Personalized Recommenders can help to find potential items and then recommend them for particular users. Conventional recommender methods always work on a rating schema that items are rated from 1 to 5. However, there are several rating Schemas (ways that items are rated) in reality, which are overlooked by conventional methods. By transforming rating Schemas into fuzzy user profiles to record users' preferences, our proposed method can deal with different system rating Schemas, and improve the scalability of recommender systems. Additionally, we incorporate user-based method with item-based collaborative methods by clustering users, which can help us to gain insight into the relationship between users. The aim of this research is to provide a new method for personalized recommendation. Our proposed method is the first to normalize the user vectors using fuzzy set theory before the k-medians clustering method is adjusted, and then to apply item-based collaborative algorithm with item vectors. To evaluate the effectiveness of our approach, the proposed algorithm is compared with two conventional collaborative filtering methods, based on MovieLens data set. As expected, our proposed method outperforms the conventional collaborative filtering methods as it can improve system scalability while maintaining accuracy.

    本文言語English
    ホスト出版物のタイトルIEEE International Conference on Fuzzy Systems
    出版社Institute of Electrical and Electronics Engineers Inc.
    ページ2171-2177
    ページ数7
    ISBN(印刷版)9781479920723
    DOI
    出版ステータスPublished - 2014 9 4
    イベント2014 IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2014 - Beijing
    継続期間: 2014 7 62014 7 11

    Other

    Other2014 IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2014
    CityBeijing
    Period14/7/614/7/11

    ASJC Scopus subject areas

    • Software
    • Artificial Intelligence
    • Applied Mathematics
    • Theoretical Computer Science

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