Applying cross-level association rule mining to cold-start recommendations

Cane Wing Ki Leung; Stephen Chi Fai Chan; Fu Lai Korris Chung

doi:10.1109/WIIATW.2007.4427557

Applying cross-level association rule mining to cold-start recommendations

Cane Wing Ki Leung, Stephen Chi Fai Chan, Fu Lai Korris Chung

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

21 Citations (Scopus)

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
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	https://doi.org/10.1109/WIIATW.2007.4427557
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
Country	United States
City	Silicon Valley, CA
Period	2/11/07 → 5/11/07

ASJC Scopus subject areas

Artificial Intelligence
Computer Networks and Communications

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10.1109/WIIATW.2007.4427557

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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.",

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Leung, CWK, Chan, SCF & Chung, FLK 2007, Applying cross-level association rule mining to cold-start recommendations. in Proceedings - 2007 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Workshops, WI-IAT Workshops 2007., 4427557, pp. 133-136, 2007 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Workshops, WI-IAT Workshops 2007, Silicon Valley, CA, United States, 2/11/07. https://doi.org/10.1109/WIIATW.2007.4427557

Applying cross-level association rule mining to cold-start recommendations. / Leung, Cane Wing Ki; Chan, Stephen Chi Fai; Chung, Fu Lai Korris.

Proceedings - 2007 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Workshops, WI-IAT Workshops 2007. 2007. p. 133-136 4427557.

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

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AB - 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.

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Applying cross-level association rule mining to cold-start recommendations

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