Coordinating Disagreement and Satisfaction in Group Formation for Recommendation

Abstract

Group recommendation has attracted significant research efforts for its importance in benefiting a group of users. There are two steps involved in this process, which are group formation and making recommendations. The studies on making recommendations to a given group has been studied extensively, however seldom investigation has been put into the essential problem of how the groups should be formed. As pointed in existing studies on group recommendation, both satisfaction and disagreement are important factors in terms of recommendation quality. Satisfaction reflects the degree to which the item is preferred by the members; while disagreement reflects the level at which members disagree with each other. As it is difficult to solve group formation problem, none of existing studies ever considered both factors in group formation.

This paper investigates the satisfaction and disagreement aware group formation problem in group recommendation. In this work, we present a formulation of the satisfaction and disagreement aware group formation problem. We design an efficient optimization algorithm based on Projected Gradient Descent and further propose a swapping alike algorithm that accommodates to large datasets. We conduct extensive experiments on real-world datasets and the results verify that the performance of our algorithm is close to optimal. More importantly, our work reveals that proper group formation can lead to better performances of group recommendation in different scenarios. To our knowledge, we are the first to study the group formation problem with satisfaction and disagreement awareness for group recommendation.