On-the-spot Knowledge Refinement for an Interactive Recommender System

This paper proposes a method to refine knowledge about items in an item database for an interactive recommender system. The proposed method is integrated into a recommender system and invoked when the system recognizes a problem with the item database from users' feedback about recommended items. The proposed method collects information from a user via similar interactions to those of a recommendation process. In this way, a user who is knowledgeable in a target domain, but does not necessarily know the internal system can participate in the knowledge refinement process. Thus, the proposed method paves the way for applying crowdsourcing to knowledge refinement.