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

Shan Xu, Junzo Watada

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

    2 Citations (Scopus)

    Abstract

    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.

    Original languageEnglish
    Title of host publicationIEEE International Conference on Fuzzy Systems
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Pages2171-2177
    Number of pages7
    ISBN (Print)9781479920723
    DOIs
    Publication statusPublished - 2014 Sep 4
    Event2014 IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2014 - Beijing
    Duration: 2014 Jul 62014 Jul 11

    Other

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

    Fingerprint

    Collaborative filtering
    User Profile
    Scalability
    Clustering
    Fuzzy set theory
    Recommender systems
    Schema
    Collaborative Filtering
    Personalized Recommendation
    Normalize
    Recommender Systems
    User Preferences
    Fuzzy Set Theory
    Clustering Methods

    ASJC Scopus subject areas

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

    Cite this

    Xu, S., & Watada, J. (2014). A method for hybrid personalized recommender based on clustering of fuzzy user profiles. In IEEE International Conference on Fuzzy Systems (pp. 2171-2177). [6891690] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/FUZZ-IEEE.2014.6891690

    A method for hybrid personalized recommender based on clustering of fuzzy user profiles. / Xu, Shan; Watada, Junzo.

    IEEE International Conference on Fuzzy Systems. Institute of Electrical and Electronics Engineers Inc., 2014. p. 2171-2177 6891690.

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

    Xu, S & Watada, J 2014, A method for hybrid personalized recommender based on clustering of fuzzy user profiles. in IEEE International Conference on Fuzzy Systems., 6891690, Institute of Electrical and Electronics Engineers Inc., pp. 2171-2177, 2014 IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2014, Beijing, 14/7/6. https://doi.org/10.1109/FUZZ-IEEE.2014.6891690
    Xu S, Watada J. A method for hybrid personalized recommender based on clustering of fuzzy user profiles. In IEEE International Conference on Fuzzy Systems. Institute of Electrical and Electronics Engineers Inc. 2014. p. 2171-2177. 6891690 https://doi.org/10.1109/FUZZ-IEEE.2014.6891690
    Xu, Shan ; Watada, Junzo. / A method for hybrid personalized recommender based on clustering of fuzzy user profiles. IEEE International Conference on Fuzzy Systems. Institute of Electrical and Electronics Engineers Inc., 2014. pp. 2171-2177
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