Collaborative filtering based on the latent class model for attributes

Manabu Kobayashi, Kenta Mikawa, Masayuki Goto, Toshiyasu Matsushima, Shigeichi Hirasawa

研究成果: Conference contribution

抄録

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.

本文言語English
ホスト出版物のタイトルProceedings - 16th IEEE International Conference on Machine Learning and Applications, ICMLA 2017
編集者Xuewen Chen, Bo Luo, Feng Luo, Vasile Palade, M. Arif Wani
出版社Institute of Electrical and Electronics Engineers Inc.
ページ893-896
ページ数4
ISBN(電子版)9781538614174
DOI
出版ステータスPublished - 2017
イベント16th IEEE International Conference on Machine Learning and Applications, ICMLA 2017 - Cancun, Mexico
継続期間: 2017 12 182017 12 21

出版物シリーズ

名前Proceedings - 16th IEEE International Conference on Machine Learning and Applications, ICMLA 2017
2017-December

Other

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

ASJC Scopus subject areas

  • 人工知能
  • コンピュータ サイエンスの応用

フィンガープリント

「Collaborative filtering based on the latent class model for attributes」の研究トピックを掘り下げます。これらがまとまってユニークなフィンガープリントを構成します。

引用スタイル