A personal news agent that talks, learns and explains
Proceedings of the third annual conference on Autonomous Agents
Content-based book recommending using learning for text categorization
DL '00 Proceedings of the fifth ACM conference on Digital libraries
Explaining collaborative filtering recommendations
CSCW '00 Proceedings of the 2000 ACM conference on Computer supported cooperative work
Content-boosted collaborative filtering for improved recommendations
Eighteenth national conference on Artificial intelligence
CinemaScreen Recommender Agent: Combining Collaborative and Content-Based Filtering
IEEE Intelligent Systems
A study of mixture models for collaborative filtering
Information Retrieval
Who is talking about what: social map-based recommendation for content-centric social websites
Proceedings of the fourth ACM conference on Recommender systems
Proceedings of the fourth ACM conference on Recommender systems
Who is Doing What and When: Social Map-Based Recommendation for Content-Centric Social Web Sites
ACM Transactions on Intelligent Systems and Technology (TIST)
Minimal and complete explanations for critical multi-attribute decisions
ADT'11 Proceedings of the Second international conference on Algorithmic decision theory
A generalized taxonomy of explanations styles for traditional and social recommender systems
Data Mining and Knowledge Discovery
Exploiting the web of data in model-based recommender systems
Proceedings of the sixth ACM conference on Recommender systems
Dimensions as Virtual Items: Improving the predictive ability of top-N recommender systems
Information Processing and Management: an International Journal
Knowledge-Based Systems
How should I explain? A comparison of different explanation types for recommender systems
International Journal of Human-Computer Studies
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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.