Link prediction approach to collaborative filtering

Zan Huang, Xin Li, Hsinchun Chen

Research output: Contribution to journalConference article

222 Scopus citations

Abstract

Recommender systems can provide valuable services in a digital library environment, as demonstrated by its commercial success in book, movie, and music industries. One of the most commonly-used and successful recommendation algorithms is collaborative filtering, which explores the correlations within user-item interactions to infer user interests and preferences. However, the recommendation quality of collaborative filtering approaches is greatly limited by the data sparsity problem. To alleviate this problem we have previously proposed graph-based algorithms to explore transitive user-item associations. In this paper, we extend the idea of analyzing user-item interactions as graphs and employ link prediction approaches proposed in the recent network modeling literature for making collaborative filtering recommendations. We have adapted a wide range of linkage measures for making recommendations. Our preliminary experimental results based on a book recommendation dataset show that some of these measures achieved significantly better performance than standard collaborative filtering algorithms.

Original languageEnglish (US)
Pages (from-to)141-142
Number of pages2
JournalProceedings of the ACM/IEEE Joint Conference on Digital Libraries
DOIs
StatePublished - Jan 1 2005
Event5th ACM/IEEE Joint Conference on Digital Libraries - Digital Libraries: Cyberinfrastructure for Research and Education - Denver, CO, United States
Duration: Jun 7 2005Jun 11 2005

Keywords

  • Collaborative filtering
  • Link prediction
  • Recommender system

ASJC Scopus subject areas

  • Engineering(all)

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