Explaining neighborhood-based recommendations
Sérgio Cleger Tamayo, Juan Manuel Fernández-Luna, Juan F. Huete · 2012
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.