On the Intrinsic Challenges of Group Recommendation

In group recommendation systems, recommendations may be given to arbitrarily composed groups that may not display any particular characteristics across group members. Since individual recommendation systems can assume that the users’ previous behavior is sufficient for coming up with new recommendations, statistical analyses of user logs or user preferences is enough for computing new recommendations with some degree of certainty. Group recommendation systems face a substantially more complex situation, as group members may be so different that no single recommendation seem acceptable and group processes may alter the individual preferences when users discuss their options. This paper discusses some of the intrinsic challenges of group recommendation systems and argue that current approaches to group recommendations only address part of the problem. A framework for analyzing the critical issues in group recommendations is presented and related to common recommendation problems.