A hybrid method using multidimensional clustering-based collaborative filtering to improve recommendation diversity

Xiaohui Li, Tomohiro Murata

研究成果: Article

1 引用 (Scopus)

抄録

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.

元の言語English
ページ(範囲)749-755
ページ数7
ジャーナルIEEJ Transactions on Electronics, Information and Systems
133
発行部数4
DOI
出版物ステータスPublished - 2013

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Collaborative filtering

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

これを引用

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