A hybrid explanations framework for collaborative filtering recommender systems

Augmenting personalized recommendations with explanations is believed to improve users' trust, loyalty, satisfaction, and recommender's persuasiveness. We present a flexible explanations framework for collaborative filtering recom-mender systems. Our algorithms utilizes item tags to automatically generate personalized explanations in a natural language format. Given a specific user and a recommended item, the algorithm utilizes the user's personal information as well as global information (e.g., item similarities, metadata) in order to rank item tags based on their "explanatory power". The top tags are chosen to construct a personalized explanation sentence which helps shed light on the underlying recommender. Our system has been well received by both focus groups as well as in expert evaluations and is scheduled to be evaluated in an online experiment.