Leveraging multiviews of trust and similarity to enhance clustering-based recommender systems

Guibing Guo, Jie Zhang, Neil Yorke-Smith

Research output: Contribution to journalArticleScientificpeer-review

94 Citations (Scopus)

Abstract

Although demonstrated to be efficient and scalable to large-scale data sets, clustering-based recommender systems suffer from relatively low accuracy and coverage. To address these issues, we develop a multiview clustering method through which users are iteratively clustered from the views of both rating patterns and social trust relationships. To accommodate users who appear in two different clusters simultaneously, we employ a support vector regression model to determine a prediction for a given item, based on user-, item-and prediction-related features. To accommodate (cold) users who cannot be clustered due to insufficient data, we propose a probabilistic method to derive a prediction from the views of both ratings and trust relationships. Experimental results on three real-world data sets demonstrate that our approach can effectively improve both the accuracy and coverage of recommendations as well as in the cold start situation, moving clustering-based recommender systems closer towards practical use.

Original languageEnglish
Pages (from-to)14-27
Number of pages14
JournalKnowledge-Based Systems
Volume74
Issue number1
DOIs
Publication statusPublished - 2015
Externally publishedYes

Keywords

  • Cold start
  • Collaborative filtering
  • Multiview clustering
  • Recommender systems
  • Similarity
  • Trust

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