Social Influence-based Group Representation Learning for Group Recommendation

As social animals, attending group activities is an indispensable part in people's daily social life, and it is an important task for recommender systems to suggest satisfying activities to a group of users. The major challenge in this task is how to aggregate personal preferences of group members to infer the decision of a group. Conventional group recommendation methods applied a predefined strategy for preference aggregation. However, these static strategies are too simple to model the real and complex process of group decision-making, especially for occasional groups which are formed ad-hoc. Moreover, group members should have non-uniform influences or weights in a group, and the weight of a user can be varied in different groups. Therefore, an ideal group recommender system should be able to accurately learn not only users' personal preferences, but also the preference aggregation strategy from data. In this paper, we propose a novel group recommender system, namely SIGR (short for Social Influence-based Group Recommender), which takes an attention mechanism and a bipartite graph embedding model BGEM as building blocks. Specifically, we adopt an attention mechanism to learn each user's social influence and adapt their social influences to different groups, and develop a novel deep social influence learning framework to exploit and integrate users' global and local social network structure information to further improve the estimation of users' social influences. BGEM is extended to model group-item interactions. In order to overcome the limitation and sparsity of the interaction data generated by occasional groups, we propose two model optimization approaches to seamlessly integrate the user-item interaction data. We create two large-scale benchmark datasets and conduct extensive experiments on them. The experimental results show the superiority of our proposed SIGR by comparing with state-of-the-art group recommender models.