Incorporating global and local social networks for group recommendations

Abstract

Due to the social nature of human beings, group activities have become an integral part of daily life. This creates the need for an in-depth study of the group-recommendation task: recommending items to a group of users. Unlike individual decision-making, which relies primarily on personal preferences, group decision-making is a process of negotiation and agreement among group members, in which social characteristics are a critical factor in achieving positive recommendation results. Therefore, in this paper, we propose a new model to solve the group recommendation problem from both global and local social networks. In a global network, a user’s social influence spreads through social connections and affects the preferences of others. In a local network, group members may contribute differently to the final decision, forming a dynamic negotiation and consensus process. We propose to model global and local networks with two components: 1) an attentive graph convolutional network based global network diffusion (GND) module to simulate the spread of social influence and capture the social gate of each user, and 2) a multi-channel attention-based local network fusion (LNF) module to learn the complex decision-making process among group members and integrate them into a final representation of the group. Finally, two separate neural collaborative filtering (NCF) modules are presented to model group-item and user-item interactions, respectively, to enhance each other. Extensive experimental results from two real-world datasets show the effectiveness of our proposed model.