逐次的ユーザ・アイテムクラスター抽出による協調フィルタリングの計算効率化

本多 克宏; 野津 亮; 市橋 秀友

doi:10.11511/jacc.53.0.230.0

THE 53RD JAPAN JOINT AUTOMATIC CONTROL CONFERENCE

Session ID : 501

DOI https://doi.org/10.11511/jacc.53.0.230.0

Conference information

Host: The Japan Society of Mechanical Engineers, The Society of Instrument and Control Engineers, The Institute of Systems, Control and Information Engineers, The Society of Chemical Engineers, Japan, The Japan Society for Precision Engineering, The Japan Society for Aeronautical and Space Sciences

Co-host: Technically Cosponsored by 50 Societies and Institutes

Neuro, Fuzzy and Reinforcement Learning

Improvement of Computational Efficiency in Collaborative Filtering Based on Sequential User-Item Cluster Extraction

*Katsuhiro Honda, Akira Notsu, Hidetomo Ichihashi

Author information

Keywords: Collaborative filtering, Fuzzy clustering, Eigenvalue problem

CONFERENCE PROCEEDINGS FREE ACCESS

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Abstract

A collaborative filtering model based on sequential user-item fuzzy co-cluster extraction was proposed for achieving high ability in personalized recommendation. It is, however, often suffered from computational cost of solving eigenvalue problem of (user + item) square matrix. This paper considers improvement of computational cost by taking account of the characteristics of the input data matrix.

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