1. An empirical study of a cross-level association rule mining approach to cold-start recommendations
- Author
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Fu-Lai Chung, Stephen C. F. Chan, and Cane Wing-Ki Leung
- Subjects
Information Systems and Management ,Association rule learning ,business.industry ,Computer science ,Recommender system ,Machine learning ,computer.software_genre ,Preference ,Management Information Systems ,Domain (software engineering) ,Empirical research ,Cold start ,Work (electrical) ,Artificial Intelligence ,Collaborative filtering ,Artificial intelligence ,business ,computer ,Software - Abstract
We propose a novel hybrid recommendation approach to address the well-known cold-start problem in Collaborative Filtering (CF). Our approach 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 present a motivating example of our work based on the model. We then describe how CLARE generates cold-start recommendations. We empirically evaluated the effectiveness of CLARE, which shows superior performance to related work in addressing the cold-start problem.
- Published
- 2008