Overcoming Data Sparsity in Group Recommendation

Abstract

It has been an important task for recommender systems to suggest satisfying activities to a group of users in peoples daily social life. The major challenge in this task is how to aggregate personal preferences of group members to infer the decision of a group. In this paper, we propose a novel end-to-end group recommender system named CAGR (short for Centrality-Aware Group Recommender), which takes the Bipartite Graph Embedding Model (BGEM), the self-attention mechanism and Graph Convolutional Networks (GCNs) as basic building blocks to learn group and user representations in a unified way. Specifically, we first extend BGEM to model group-item interactions, and then in order to overcome the sparsity of the interaction data generated by occasional groups, we propose a self-attentive mechanism to represent groups based on the group members. To further alleviate the group data sparsity problem, we propose two model optimization approaches to exploit an and integrate the user-item interaction data. To overcome the sparsity issue of user-item interaction data, we extend GCNs to leverage the social network to enhance user representation learning. We create two large-scale benchmark datasets and conduct extensive experiments on them. The experimental results show the superiority of our proposed CAGR.