Collaborative filtering analysis of consumption behavior based on the latent class model

Manabu Kobayashi, Kenta Mikawa, Masayuki Goto, Shigeichi Hirasawa

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

    Abstract

    In this manuscript, we investigate a collaborative filtering method to characterize consumption behavior (or evaluation) of customers (or users) and services (or items) for marketing. Assuming that each customer and service have the invisible attribute, which is called latent class, we propose a new Bayesian statistical model that consumption behavior is probabilistically arise based on a latent class combination of a customer and service. Then, we show the method to estimate parameters of a statistical model based on the variational Bayes method and the mean field approximation. Consequently, we show the effectiveness of the proposed model and the estimation method by simulation and analyzing actual data.

    Original languageEnglish
    Title of host publication2017 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2017
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Pages1926-1931
    Number of pages6
    Volume2017-January
    ISBN (Electronic)9781538616451
    DOIs
    Publication statusPublished - 2017 Nov 27
    Event2017 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2017 - Banff, Canada
    Duration: 2017 Oct 52017 Oct 8

    Other

    Other2017 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2017
    CountryCanada
    CityBanff
    Period17/10/517/10/8

    Keywords

    • Collaborative filtering
    • Electric commerce
    • Latent class model
    • Mean field approximation
    • Variational Bayes method

    ASJC Scopus subject areas

    • Artificial Intelligence
    • Computer Science Applications
    • Human-Computer Interaction
    • Control and Optimization

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