An empirical study of a cross-level association rule mining approach to cold-start recommendations
| Autor: | Fu-Lai Chung, Stephen C. F. Chan, Cane Wing-Ki Leung |
|---|---|
| Rok vydání: | 2008 |
| Předmět: |
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 |
| Zdroj: | Knowledge-Based Systems. 21:515-529 |
| ISSN: | 0950-7051 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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