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Applying Cross-Level Association Rule Mining to Cold-Start Recommendations
| Content Provider | CiteSeerX |
|---|---|
| Author | Leung, Cane Wing-Ki Chan, Stephen Chi-Fai Chung, Fu-Lai |
| Abstract | We propose a novel hybrid recommendation algorithm for addressing the well-known cold-start problem in Collaborative Filtering (CF). Our algorithm 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 useritem and item-item relationships in recommender systems, and then describe how the CLARE algorithm generates recommendations for cold-start items based on the preference model. Experimental results validated that CLARE is capable of recommending cold-start items, and that it increases the number of recommendable items significantly by addressing the cold-start problem. 1. |
| File Format | |
| Access Restriction | Open |
| Subject Keyword | Cold-start Recommendation Cross-level Association Rule Mining Cold-start Item Preference Model Item-item Relationship Collaborative Filter Collaborative Filtering Recommendable Item Content Information Cold-start Problem Clare Algorithm Generates Recommendation Recommender System Domain Item Novel Hybrid Recommendation Algorithm Cross-level Association Rule Well-known Cold-start Problem |
| Content Type | Text |
