Group Preference Aggregation: A Nash Equilibrium Approach

Group-oriented services such as group recommendations aim to provide services for a group of users. For these applications, how to aggregate the preferences of different group members is the toughest yet most important problem. Inspired by game theory, in this paper, we propose to explore the idea of Nash equilibrium to simulate the selections of members in a group by a game process. Along this line, we first compute the preferences (group-dependent optimal selections) of each individual member in a given group scene, i.e., an equilibrium solution of this group, with the help of two pruning approaches. Then, to get the aggregated unitary preference of each group from all group members, we design a matrix factorization-based method which aggregates the preferences in latent space and estimates the final group preference in rating space. After obtaining the group preference, group-oriented services (e.g., group recommendation) can be directly provided. Finally, we construct extensive experiments on two real-world data sets from multiple aspects. The results clearly demonstrate the effectiveness of our method.