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 user-item 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.
Original language | English |
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Title of host publication | Proceedings - 2007 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Workshops, WI-IAT Workshops 2007 |
Pages | 133-136 |
Number of pages | 4 |
DOIs | |
Publication status | Published - 1 Dec 2007 |
Event | 2007 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Workshops, WI-IAT Workshops 2007 - Silicon Valley, CA, United States Duration: 2 Nov 2007 → 5 Nov 2007 |
Conference
Conference | 2007 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Workshops, WI-IAT Workshops 2007 |
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Country/Territory | United States |
City | Silicon Valley, CA |
Period | 2/11/07 → 5/11/07 |
ASJC Scopus subject areas
- Artificial Intelligence
- Computer Networks and Communications