Collaborative filtering based on the latent class model for attributes

Manabu Kobayashi, Kenta Mikawa, Masayuki Goto, Toshiyasu Matsushima, 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 of customers and services with various attributes for marketing. We assume that each customer and service have the invisible attribute which is called latent class. Assuming a combination of attribute values of a customer and service is classified to a latent class, furthermore, we propose a new Bayesian statistical model that consumption behavior is probabilistically arise based on a latent class combination of a customer, service and attribute values. 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.

Original languageEnglish
Title of host publicationProceedings - 16th IEEE International Conference on Machine Learning and Applications, ICMLA 2017
EditorsXuewen Chen, Bo Luo, Feng Luo, Vasile Palade, M. Arif Wani
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages893-896
Number of pages4
ISBN (Electronic)9781538614174
DOIs
Publication statusPublished - 2017 Jan 1
Event16th IEEE International Conference on Machine Learning and Applications, ICMLA 2017 - Cancun, Mexico
Duration: 2017 Dec 182017 Dec 21

Publication series

NameProceedings - 16th IEEE International Conference on Machine Learning and Applications, ICMLA 2017
Volume2017-December

Other

Other16th IEEE International Conference on Machine Learning and Applications, ICMLA 2017
CountryMexico
CityCancun
Period17/12/1817/12/21

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Keywords

  • collaborative filtering
  • electric commerce
  • latent class model
  • mean field approximation
  • variational Bayes method

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications

Cite this

Kobayashi, M., Mikawa, K., Goto, M., Matsushima, T., & Hirasawa, S. (2017). Collaborative filtering based on the latent class model for attributes. In X. Chen, B. Luo, F. Luo, V. Palade, & M. A. Wani (Eds.), Proceedings - 16th IEEE International Conference on Machine Learning and Applications, ICMLA 2017 (pp. 893-896). (Proceedings - 16th IEEE International Conference on Machine Learning and Applications, ICMLA 2017; Vol. 2017-December). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICMLA.2017.00-42