Case-based group recommendation: Compromising for success

Abstract

There are increasingly many recommendation scenarios where recommendations must be made to satisfy groups of people rather than individuals. This represents a significant challenge for current recommender systems because they must now cope with the potentially conflicting preferences of multiple users when selecting items for recommendation. In this paper we focus on how individual user models can be aggregated to produce a group model for the purpose of biasing recommendations in a critiquing-based, case-based recommender. We describe and evaluate 3 different aggregation policies and highlight the benefits of group recommendation using live-user preference data. © Springer-Verlag Berlin Heidelberg 2007.