Explaining collaborative filtering recommendations
CSCW '00 Proceedings of the 2000 ACM conference on Computer supported cooperative work
A generalized taxonomy of explanations styles for traditional and social recommender systems
Data Mining and Knowledge Discovery
Being confident about the quality of the predictions in recommender systems
ECIR'13 Proceedings of the 35th European conference on Advances in Information Retrieval
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Recommender Systems (RS) attempt to discover users' preferences, and to learn about them in order to anticipate their needs. The main task normally associated with a RS is to offer suggestions for items. However, for most users, RSs are black boxes, computerized oracles that give advice, but cannot be questioned. In order to improve the quality of predictions and the satisfaction of the users, explanations facilities are needed. We present a novel methodology to explain recommendations: showing predictions over a set of observed items. Our proposal has been validated by means of user studies and lab experiments using MovieLens dataset.