A recommendation system by collaborative filtering including information and characteristics on users and items

Manami Kawasaki, Takashi Hasuike

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

    4 Citations (Scopus)

    Abstract

    In this research, a revised recommendation system to generate recommended items for each user is constructed, such as 'recommended for you' on the e-commerce websites. By using both the purchase and the browsing data, sparseness of matrix derived from the user's behavior history data is reduced. The main purpose is to construct a recommendation system that can recommend new items not browsed by users and appropriate items matching user preferences. As a procedure for generating recommended items, a user-item matrix and must-link constraints are first constructed from user's behavior history data. We add rows and columns to represent various item and user information to the user-item matrix. Next, semi-supervised learning is performed using the user-item matrix and the must-link constraint, and a new user-item matrix is generated. From this matrix on the basis of Pearson similarity, item similarity and user similarity are obtained. Finally, item-based collaborative filtering and user-based collaborative filtering are performed to generate recommended items. Experimental results show that the F-measure to represent the recommendation accuracy increases by generating recommended items with the proposed model using must-link constraints, user information and item information. In addition, it can be seen that the proposed model is more likely to purchase recommended items than the model of existing models.

    Original languageEnglish
    Title of host publication2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Pages1-8
    Number of pages8
    Volume2018-January
    ISBN (Electronic)9781538627259
    DOIs
    Publication statusPublished - 2018 Feb 2
    Event2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Honolulu, United States
    Duration: 2017 Nov 272017 Dec 1

    Other

    Other2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017
    CountryUnited States
    CityHonolulu
    Period17/11/2717/12/1

    Fingerprint

    Collaborative filtering
    Recommendation System
    Collaborative Filtering
    Recommender systems
    History
    Supervised learning
    User Behavior
    Websites
    Semi-supervised Learning
    User Preferences
    Browsing
    Electronic Commerce
    Model
    Recommendations
    Likely

    Keywords

    • item-based collaborative filtering
    • must-link constraint
    • recommendation system
    • user-based collaborative filtering

    ASJC Scopus subject areas

    • Artificial Intelligence
    • Computer Science Applications
    • Control and Optimization

    Cite this

    Kawasaki, M., & Hasuike, T. (2018). A recommendation system by collaborative filtering including information and characteristics on users and items. In 2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings (Vol. 2018-January, pp. 1-8). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SSCI.2017.8280983

    A recommendation system by collaborative filtering including information and characteristics on users and items. / Kawasaki, Manami; Hasuike, Takashi.

    2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings. Vol. 2018-January Institute of Electrical and Electronics Engineers Inc., 2018. p. 1-8.

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

    Kawasaki, M & Hasuike, T 2018, A recommendation system by collaborative filtering including information and characteristics on users and items. in 2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings. vol. 2018-January, Institute of Electrical and Electronics Engineers Inc., pp. 1-8, 2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017, Honolulu, United States, 17/11/27. https://doi.org/10.1109/SSCI.2017.8280983
    Kawasaki M, Hasuike T. A recommendation system by collaborative filtering including information and characteristics on users and items. In 2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings. Vol. 2018-January. Institute of Electrical and Electronics Engineers Inc. 2018. p. 1-8 https://doi.org/10.1109/SSCI.2017.8280983
    Kawasaki, Manami ; Hasuike, Takashi. / A recommendation system by collaborative filtering including information and characteristics on users and items. 2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings. Vol. 2018-January Institute of Electrical and Electronics Engineers Inc., 2018. pp. 1-8
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