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 proceedingConference article published in proceeding or bookAcademic researchpeer-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 languageEnglish
Title of host publicationProceedings - 2007 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Workshops, WI-IAT Workshops 2007
Pages133-136
Number of pages4
DOIs
Publication statusPublished - 1 Dec 2007
Event2007 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 20075 Nov 2007

Conference

Conference2007 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Workshops, WI-IAT Workshops 2007
CountryUnited States
CitySilicon Valley, CA
Period2/11/075/11/07

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications

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