Explaining collaborative filtering recommendations
CSCW '00 Proceedings of the 2000 ACM conference on Computer supported cooperative work
The role of transparency in recommender systems
CHI '02 Extended Abstracts on Human Factors in Computing Systems
Improving recommendation lists through topic diversification
WWW '05 Proceedings of the 14th international conference on World Wide Web
An empirical study of the influence of argument conciseness on argument effectiveness
ACL '00 Proceedings of the 38th Annual Meeting on Association for Computational Linguistics
Rule Based Expert Systems: The Mycin Experiments of the Stanford Heuristic Programming Project (The Addison-Wesley series in artificial intelligence)
Explanation in Recommender Systems
Artificial Intelligence Review
Being accurate is not enough: how accuracy metrics have hurt recommender systems
CHI '06 Extended Abstracts on Human Factors in Computing Systems
A Survey of Explanations in Recommender Systems
ICDEW '07 Proceedings of the 2007 IEEE 23rd International Conference on Data Engineering Workshop
A personalized system for conversational recommendations
Journal of Artificial Intelligence Research
The Effectiveness of Personalized Movie Explanations: An Experiment Using Commercial Meta-data
AH '08 Proceedings of the 5th international conference on Adaptive Hypermedia and Adaptive Web-Based Systems
The effects of transparency on trust in and acceptance of a content-based art recommender
User Modeling and User-Adapted Interaction
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This thesis investigates the properties of a good explanation in a movie recommender system. Beginning with a summarized literature review, we suggest seven criteria for evaluation of explanations in recommender systems. This is followed by an attempt to define the properties of a useful explanation, using a movie review corpus and focus groups. We conclude with planned experiments and evaluation.