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

Manabu Kobayashi; Kenta Mikawa; Masayuki Goto; Shigeichi Hirasawa

doi:10.1109/SMC.2017.8122899

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 proceeding › Conference 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 language	English
Title of host publication	2017 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2017
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1926-1931
Number of pages	6
Volume	2017-January
ISBN (Electronic)	9781538616451
DOIs	https://doi.org/10.1109/SMC.2017.8122899
Publication status	Published - 2017 Nov 27
Event	2017 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2017 - Banff, Canada Duration: 2017 Oct 5 → 2017 Oct 8

Other

Other	2017 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2017
Country/Territory	Canada
City	Banff
Period	17/10/5 → 17/10/8

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

Access to Document

10.1109/SMC.2017.8122899

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Collaborative filtering analysis of consumption behavior based on the latent class model. / Kobayashi, Manabu; Mikawa, Kenta; Goto, Masayuki et al.
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 proceeding › Conference 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

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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.",

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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.

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

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Keywords

ASJC Scopus subject areas

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