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

Xiaohui Li*, Tomohiro Murata

*この研究の対応する著者

研究成果査読

1 被引用数 (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.

本文言語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
4
DOI
出版ステータスPublished - 2013 1 1

ASJC Scopus subject areas

  • 電子工学および電気工学

フィンガープリント

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