Towards more conversational and collaborative recommender systems

Current recommender systems, based on collaborative filtering, implement a rather limited model of interaction. These systems intelligently elicit information from a user only during the initial registration phase. Furthermore, users tend to collaborate only indirectly. We believe there are several unexplored opportunities in which information can be effectively elicited from users by making the underlying interaction model more conversational and collaborative. In this paper, we propose a set of techniques to intelligently select what information to elicit from the user in situations in which the user may be particularly motivated to provide such information. We argue that the resulting interaction improves the user experience. We conclude by reporting results of an offline experiment in which we compare the influence of different elicitation techniques on both the accuracy of the system’s predictions and the user’s effort. Keywords Recommender systems, Collaborative filtering, P...

• Type: Conference
• Year: 2003
• Where: IUI
• Authors: Giuseppe Carenini, Jocelyin Smith, David Poole
• Keywords: Collaborative Filtering, Current Recommender Systems, IUI 2003, Recommender Systems, Software Engineering