The recommender system is of great significance to alleviate information overload. The rise of online social networks leads to a promising direction—social recommendation. By injecting the interaction influence among social users, recommendation performance has been further improved. Successful as they are, we argue that most social recommendation methods are still not sufficient to make full use of social network information. Existing solutions typically either considered only the local neighbors or treat neighbors’ information equally, even or both. However, few studies have attempted to solve these social recommendation problems jointly from both the perspective of social depth and social strength. Recently, graph convolutional neural networks have shown great potential in learning graph data by modeling the information propagation and aggregation process. Thus, we propose an attention-based social aggregation neural networks (abbreviated as SAN) model to build a recommendation system. Different from previous work, our proposed SAN model simulates the recursive social aggregation process to spread the global social influence, and simultaneously introduces social attention mechanism to incorporate the heterogeneous influences for better model user embedding. Instead of a shallow linear interaction function, we adopt multi-layer perception to model the complex user–item interaction. Extensive experiments on two real-world datasets show the effectiveness of our proposed model SAN, and further analysis verifies the generalization and flexibility of the model.

中文翻译：

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SAN：用于推荐系统的基于注意力的社交聚合神经网络

推荐系统对于缓解信息过载具有重要意义。在线社交网络的兴起带来了一个很有前景的方向——社交推荐。通过注入社交用户之间的交互影响力，进一步提升了推荐性能。尽管它们很成功，但我们认为大多数社交推荐方法仍然不足以充分利用社交网络信息。现有的解决方案通常要么只考虑本地邻居，要么平等地对待邻居的信息，甚至或两者兼而有之。然而，很少有研究尝试从社会深度和社会强度的角度共同解决这些社会推荐问题。最近，图卷积神经网络通过对信息传播和聚合过程进行建模，在学习图数据方面显示出巨大的潜力。因此，我们提出一个基于注意力的社交聚合神经网络（简称SAN）模型构建推荐系统。与以往的工作不同，我们提出的 SAN 模型模拟递归社交聚合过程以传播全球社交影响，同时引入社交注意机制以整合异构影响以更好地模型用户嵌入。我们采用多层感知来模拟复杂的用户-项目交互，而不是浅层的线性交互函数。在两个真实世界数据集上的大量实验表明了我们提出的模型 SAN 的有效性，进一步的分析验证了模型的泛化性和灵活性。