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

Authors:
Cane Wing-ki Leung;Stephen Chi-fai Chan;Fu-lai Chung
Affiliations:
Department of Computing, The Hong Kong Polytechnic University, Hong Kong;Department of Computing, The Hong Kong Polytechnic University, Hong Kong;Department of Computing, The Hong Kong Polytechnic University, Hong Kong
Venue:
Knowledge-Based Systems
Year:
2008

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Abstract

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.