社交影响增强的图神经网络推荐方法

Graph Neural Network Recommendation Based on Enhanced Social Influence

随着在线社交平台的发展,社交推荐已成为推荐系统中的一个重要任务.然而,用户间的社交关系通常具有稀疏性,这在一定程度上限制推荐系统的性能.为此,文中提出社交影响增强的图神经网络推荐方法,旨在利用用户之间的隐式社交关系增强社交推荐的效果.首先,分析用户与物品之间的交互数据,揭示隐含的社交关系,重构用户间的社交图.在此基础上,利用互信息最大化方法,有效融合社交图的全局特征与用户的局部特征.同时,将可学习机制融入图注意力网络中,充分捕获用户和物品间的交互信息.最后,提出一种改进的贝叶斯个性化排序损失,为评分预测任务提供准确的用户特征表示和物品特征表示.在3个公开数据集上的实验表明,文中方法性能较优.

With the rapid development of online social platforms,social recommendation becomes a critical task in recommender systems.However,the performance of recommendation systems is limited to some extent due to the sparsity of social relationships between users.Therefore,a graph neural network recommendation method based on enhanced social influence is proposed in the paper,aiming to utilize implicit social relationships between users to enhance social recommendation.The implicit social relationships are revealed,and the social graph among users is reconstructed by analyzing interaction information between users and items.On this basis,global features of the social graph are integrated with local features of users effectively via the mutual information maximization method.A learnable mechanism is integrated into the graph attention network to fully capture the interaction information between users and items.An improved Bayesian personalized ranking loss is designed to provide more accurate user and item feature representations for the rating prediction task.Extensive experiments on three public social recommendation datasets demonstrate the superiority of the proposed method.