A Hybrid Method Using Multidimensional Clustering-based Collaborative Filtering to Improve Recommendation Diversity

カテゴリ: 論文誌(論文単位)

グループ名: 【C】電子・情報・システム部門

発行日: 2013/04/01

タイトル(英語): A Hybrid Method Using Multidimensional Clustering-based Collaborative Filtering to Improve Recommendation Diversity

著者名: Xiaohui Li (Graduate School of Information, Production and Systems, Waseda University), Tomohiro Murata (Graduate School of Information, Production and Systems, Waseda University)

著者名(英語): Xiaohui Li (Graduate School of Information, Production and Systems, Waseda University), Tomohiro Murata (Graduate School of Information, Production and Systems, Waseda University)

キーワード: recommender systems，collaborative filtering，multidimensional clustering，recommendation diversity

要約(英語): This paper describes a hybrid recommendation approach for discovering individual users' potential preferences from multidimensional clustering view. The proposed approach aims to help users reach a decision to meet their diverse demands and provide the target user with highly idiosyncratic or more diverse recommendations. To this end, we propose a hybrid approach that incorporates multidimensional clustering into a collaborative filtering recommendation model to provide a quality recommendation. The proposed approach also provides a flexible solution for improving recommendation diversity and achieves a tradeoff between recommendation accuracy and diversity. The performance of proposed approach is evaluated using a public movie dataset and compared with two representative recommendation algorithms. The empirical results demonstrate that our proposed approach performs superiorly on increasing recommendation diversity while maintaining recommendation accuracy.

本誌: 電気学会論文誌C（電子・情報・システム部門誌） Vol.133 No.4 （2013） 特集：新たなサービス社会に貢献する情報・システム技術

本誌掲載ページ: 749-755 p

原稿種別: 論文／英語

電子版へのリンク: https://www.jstage.jst.go.jp/article/ieejeiss/133/4/133_749/_article/-char/ja/