Group Recommendations: Approaches and Evaluation

Abstract

Recommender systems have been an active research topic for the past decade. However, most of previous studies have focused on recommendations to a single user. Recently, as group recommender systems have emerged as an expedient field, several interesting approaches have been proposed. However, despite all of these advances, the current generation of group recommendation approaches still needs further improvements to make more effective recommendations. In this paper, we discuss the limitations of existing group recommendation approaches and present possible developments that could lead to provide better group recommendations. We perform extensive experiments with different group recommendation approaches. The results show that the performance of that group recommendation approaches is limited either by the group type or group size and no single approach consistently performs better than the other approaches. The unavailability of real-life data sets uncovers the doubts for the accuracy of evaluation; the lack of standard terminology/procedure for evaluation also could lead to poor evaluation.