ABSTRACT
Providing justification to a recommendation gives credibility to a recommender system. Some recommender systems (Amazon.com etc.) try to explain their recommendations, in an effort to regain customer acceptance and trust. But their explanations are poor, because they are based solely on rating data, ignoring the content data. Our prototype system MoviExplain is a movie recommender system that provides both accurate and justifiable recommendations.
- Bilgic, M. and Mooney, R.J. Explaining Recommendations: Satisfaction vs. Promotion. In Proceedings of the Recommender Systems Workshop (IUI Conference), 2005.Google Scholar
- Billsus, D. and Pazzani, M. A personal news agent that talks, learns and explains. In Proceedings of the Autonomous Agents Conference, pages 268-275, 1999. Google ScholarDigital Library
- Herlocker, J. and Konstan, J. and Riedl, J. Explaining collaborative filtering recommendations. In Proccedings of the Computer Supported Cooperative Work Conference, pages 241-250, 2000. Google ScholarDigital Library
- Jin, R. and Si, L. and Zhai, C. A study of mixture models for collaborative filtering. Information Retrieval, vol. 9, issue 3, pages 357-382, 2006. Google ScholarDigital Library
- Melville, P. and Mooney, R. J. and Nagarajan, R. Content-Boosted Collaborative Filtering for Improved Recommendations. In Proceedings of the AAAI Conference, pages 187-192, 2002. Google ScholarDigital Library
- Mooney, R. and Roy, L. Content-based book recommending using learning for text categorization. In Proceedings of the ACM DL Conference, pages 195-204, 2000. Google ScholarDigital Library
- Salter, J. and Antonopoulos, N. CinemaScreen Recommender Agent: Combining Collaborative and Content-Based Filtering. Intelligent Systems Magazine, vol. 21, issue 1, pages 35-41, 2006. Google ScholarDigital Library
Index Terms
- MoviExplain: a recommender system with explanations
Recommendations
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