Recent years have witnessed the rapid development of online social platforms, which effectively support the business intelligence and provide services for massive users. Along this line, large efforts have been made on the social-aware recommendation task, i.e., leveraging social contextual information to improve recommendation performance. Most existing methods have treated social relations in a static way, but the dynamic influence of social contextual information on users’ consumption choices has been largely unexploited. To that end, in this paper, we conduct a comprehensive study to reveal the dynamic social influence on users’ preferences, and then we propose a deep model called Dynamic Social-Aware Recommender System (DSRS) to integrate the users’ structural and temporal social contexts to address the dynamic social-aware recommendation task. DSRS consists of two main components, i.e., the social influence learning (SIL) and dynamic preference learning (DPL). Specifically, in the SIL module, we arrange social graphs in a sequential order and borrow the power of graph convolution networks (GCNs) to learn social context. Moreover, we design a structural-temporal attention mechanism to discriminatively model the structural social influence and the temporal social influence. Then, in the DPL part, users’ individual preferences are learned dynamically by recurrent neural networks (RNNs). Finally, with a prediction layer, we combine the users’ social context and dynamic preferences to generate recommendations. We conduct extensive experiments on two real-world datasets, and the experimental results demonstrate the superiority and effectiveness of our proposed model compared with the state-of-the-art methods.

中文翻译：

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利用结构和时间影响进行动态社会感知推荐

近年来，在线社交平台快速发展，有效支撑商业智能，为海量用户提供服务。沿着这条路线，已经在社会感知推荐任务上做出了巨大努力，即利用社会上下文信息来提高推荐性能。大多数现有方法都以静态方式处理社会关系，但社会背景信息对用户消费选择的动态影响在很大程度上未被利用。为此，在本文中，我们进行了一项综合研究，以揭示社会对用户偏好的动态影响，然后我们提出了一个称为动态社会感知推荐系统（DSRS）的深度模型，以整合用户的结构和时间社会背景，以解决动态社会感知推荐任务。DSRS 由两个主要部分组成，即社会影响学习（SIL）和动态偏好学习（DPL）。具体来说，在 SIL 模块中，我们按顺序排列社交图并借用图卷积网络 (GCN) 的力量来学习社交上下文。此外，我们设计了一种结构-时间注意机制来对结构性社会影响和时间社会影响进行区分建模。然后，在 DPL 部分，通过循环神经网络 (RNN) 动态学习用户的个人偏好。最后，通过一个预测层，我们结合用户的社会背景和动态偏好来生成推荐。我们对两个真实世界的数据集进行了大量实验，实验结果证明了我们提出的模型与最先进的方法相比的优越性和有效性。