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Content Provider | IEEE Xplore Digital Library |
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Author | Leung, C.W.-k. Chan, S.C.-f. Fu-lai Chung |
Copyright Year | 2007 |
Description | Author affiliation: Univ. Hung Horn, Hong kong (Leung, C.W.-k.; Chan, S.C.-f.; Fu-lai Chung) |
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. |
Starting Page | 133 |
Ending Page | 136 |
File Size | 403418 |
Page Count | 4 |
File Format | |
ISBN | 0769530281 |
DOI | 10.1109/WI-IATW.2007.22 |
Language | English |
Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Publisher Date | 2007-11-05 |
Publisher Place | USA |
Access Restriction | Subscribed |
Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Subject Keyword | Fuzzy sets Conferences Collaboration Filtering algorithms Collaborative filteringHybrid recommender systemsCold-start problemAssociation rule mining Information filters Information filtering Association rules Data mining Recommender systems Intelligent agent |
Content Type | Text |
Resource Type | Article |
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