Disparity-Aware Group Formation for Recommendation

Group recommendation has attracted significant research efforts for its importance in benefiting a group of users, however, seldom investigation has been put into the essential problem of how the groups should be formed. This paper investigates the disparity-aware group formation problem in group recommendation. In this work, we present a formulation of the disparity-aware group formation problem, and further show its NP-Hardness. For the case when group satisfaction is maximized, we propose a cutting plane algorithm based on bilinear program that achieves a ε approximation to the optimum. For the general case, we design an efficient optimization algorithm based on Projected Gradient Descent and further propose a simplified swapping alike algorithm that accommodates to large datasets. We conduct extensive experiments on both simulated and real-world datasets. Experimental results verify that the performance of our algorithm is close to the optima. 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 disparity awareness for recommendation, and more promising works are expected.