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

    Fingerprint

    Latent Class Model
    Collaborative filtering
    Collaborative Filtering
    Latent Class
    Customers
    Statistical Model
    Variational Bayes
    Bayes Method
    Marketing
    Mean-field Approximation
    Bayesian Model
    Variational Methods
    Attribute
    Model-based
    Evaluation
    Estimate
    Statistical Models
    Simulation
    Model

    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

    Cite this

    Kobayashi, M., Mikawa, K., Goto, M., & Hirasawa, S. (2017). Collaborative filtering analysis of consumption behavior based on the latent class model. In 2017 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2017 (Vol. 2017-January, pp. 1926-1931). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SMC.2017.8122899

    Collaborative filtering analysis of consumption behavior based on the latent class model. / Kobayashi, Manabu; Mikawa, Kenta; Goto, Masayuki; Hirasawa, Shigeichi.

    2017 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2017. Vol. 2017-January Institute of Electrical and Electronics Engineers Inc., 2017. p. 1926-1931.

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

    Kobayashi, M, Mikawa, K, Goto, M & Hirasawa, S 2017, Collaborative filtering analysis of consumption behavior based on the latent class model. in 2017 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2017. vol. 2017-January, Institute of Electrical and Electronics Engineers Inc., pp. 1926-1931, 2017 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2017, Banff, Canada, 17/10/5. https://doi.org/10.1109/SMC.2017.8122899
    Kobayashi M, Mikawa K, Goto M, Hirasawa S. Collaborative filtering analysis of consumption behavior based on the latent class model. In 2017 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2017. Vol. 2017-January. Institute of Electrical and Electronics Engineers Inc. 2017. p. 1926-1931 https://doi.org/10.1109/SMC.2017.8122899
    Kobayashi, Manabu ; Mikawa, Kenta ; Goto, Masayuki ; Hirasawa, Shigeichi. / Collaborative filtering analysis of consumption behavior based on the latent class model. 2017 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2017. Vol. 2017-January Institute of Electrical and Electronics Engineers Inc., 2017. pp. 1926-1931
    @inproceedings{98eab9a6f9574e0f9e9726b2bdc00e36,
    title = "Collaborative filtering analysis of consumption behavior based on the latent class model",
    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.",
    keywords = "Collaborative filtering, Electric commerce, Latent class model, Mean field approximation, Variational Bayes method",
    author = "Manabu Kobayashi and Kenta Mikawa and Masayuki Goto and Shigeichi Hirasawa",
    year = "2017",
    month = "11",
    day = "27",
    doi = "10.1109/SMC.2017.8122899",
    language = "English",
    volume = "2017-January",
    pages = "1926--1931",
    booktitle = "2017 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2017",
    publisher = "Institute of Electrical and Electronics Engineers Inc.",

    }

    TY - GEN

    T1 - Collaborative filtering analysis of consumption behavior based on the latent class model

    AU - Kobayashi, Manabu

    AU - Mikawa, Kenta

    AU - Goto, Masayuki

    AU - Hirasawa, Shigeichi

    PY - 2017/11/27

    Y1 - 2017/11/27

    N2 - 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.

    AB - 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.

    KW - Collaborative filtering

    KW - Electric commerce

    KW - Latent class model

    KW - Mean field approximation

    KW - Variational Bayes method

    UR - http://www.scopus.com/inward/record.url?scp=85044187606&partnerID=8YFLogxK

    UR - http://www.scopus.com/inward/citedby.url?scp=85044187606&partnerID=8YFLogxK

    U2 - 10.1109/SMC.2017.8122899

    DO - 10.1109/SMC.2017.8122899

    M3 - Conference contribution

    VL - 2017-January

    SP - 1926

    EP - 1931

    BT - 2017 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2017

    PB - Institute of Electrical and Electronics Engineers Inc.

    ER -