Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/23970
Title: An empirical study of a cross-level association rule mining approach to cold-start recommendations
Authors: Leung, CWK
Chan, SCF 
Chung, FL 
Keywords: Association rule mining
Cold-start problem
Collaborative filtering
Recommender systems
Issue Date: 2008
Publisher: Elsevier Science Bv
Source: Knowledge-based systems, 2008, v. 21, no. 7, p. 515-529 How to cite?
Journal: Knowledge-Based Systems 
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.
URI: http://hdl.handle.net/10397/23970
DOI: 10.1016/j.knosys.2008.03.012
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