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

Xiaohui Li, Tomohiro Murata

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)749-755
Number of pages7
JournalIEEJ Transactions on Electronics, Information and Systems
Volume133
Issue number4
DOIs
Publication statusPublished - 2013 Jan 1

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Keywords

  • Collaborative filtering
  • Multidimensional clustering
  • Recommendation diversity
  • Recommender systems

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

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