近日，收到国际会议ICONIP 2020会议组委会通知，博士生刘海峰的论文《Improving Social Recommendations with Item Relationships》被该会议录用为长文，该会议全称是International Conference on Neural Information Processing，是中国计算机学会推荐C类国际会议，该研究主要关注于推荐领域，提出一种图神经网络模型，解决了社会化推荐问题，刘海峰博士在线上会议中做了论文汇报，并与同行展开了深入的交流。
Social recommendations have witnessed rapid developments for improving the performance of recommender systems, due to the growing influence of social networks. However, existing social recommendations often ignore to facilitate the substitutable and complementary items to understand items and enhance the recommender systems. We propose a novel graph neural network framework to model the multi-graph data (user-item graph, user-user graph, item-item graph) in social recommendations. In particular, we introduce a viewpoint mechanism to model the relationship between users and items. We conduct an extensive experiment on two public benchmarks, demonstrating significant improvement over several state-of-the-art models.