An empirical study of a cross-level association rule mining approach to cold-start recommendations

Cane Wing ki Leung, Stephen Chi fai Chan, Fu Lai Korris Chung

Research output: Journal article publicationJournal articleAcademic researchpeer-review

70 Citations (Scopus)


We propose a novel hybrid recommendation approach to address the well-known cold-start problem in Collaborative Filtering (CF). Our approach makes use of Cross-Level Association RulEs (CLARE) to integrate content information about domain items into collaborative filters. We first introduce a preference model comprising both user-item and item-item relationships in recommender systems, and present a motivating example of our work based on the model. We then describe how CLARE generates cold-start recommendations. We empirically evaluated the effectiveness of CLARE, which shows superior performance to related work in addressing the cold-start problem.
Original languageEnglish
Pages (from-to)515-529
Number of pages15
JournalKnowledge-Based Systems
Issue number7
Publication statusPublished - 1 Oct 2008


  • Association rule mining
  • Cold-start problem
  • Collaborative filtering
  • Recommender systems

ASJC Scopus subject areas

  • Management Information Systems
  • Software
  • Information Systems and Management
  • Artificial Intelligence

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