Most of the existing deep learning-based sequential recommendation approaches utilize the recurrent neural network architecture or self-attention to model the sequential patterns and temporal influence among a user's historical behavior and learn the user's preference at a specific time. However, these methods have two main drawbacks. First, they focus on modeling users' dynamic states from a user-centric perspective and always neglect the dynamics of items over time. Second, most of them deal with only the first-order user-item interactions and do not consider the high-order connectivity between users and items, which has recently been proved helpful for the sequential recommendation. To address the above problems, in this article, we attempt to model user-item interactions by a bipartite graph structure and propose a new recommendation approach based on a Position-enhanced and Time-aware Graph Convolutional Network (PTGCN) for the sequential recommendation. PTGCN models the sequential patterns and temporal dynamics between user-item interactions by defining a position-enhanced and time-aware graph convolution operation and learning the dynamic representations of users and items simultaneously on the bipartite graph with a self-attention aggregator. Also, it realizes the high-order connectivity between users and items by stacking multi-layer graph convolutions. To demonstrate the effectiveness of PTGCN, we carried out a comprehensive evaluation of PTGCN on three real-world datasets of different sizes compared with a few competitive baselines. Experimental results indicate that PTGCN outperforms several state-of-the-art models in terms of two commonly-used evaluation metrics for ranking.

中文翻译：

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用于顺序推荐的位置增强和时间感知图卷积网络

大多数现有的基于深度学习的序列推荐方法利用循环神经网络架构或自注意力来模拟用户历史行为之间的序列模式和时间影响，并了解用户在特定时间的偏好。然而，这些方法有两个主要缺点。首先，他们专注于从以用户为中心的角度对用户的动态状态进行建模，而总是忽略项目随时间的动态变化。其次，它们中的大多数只处理一阶用户-项目交互，没有考虑用户和项目之间的高阶连接，这最近被证明有助于顺序推荐。针对以上问题，在本文中，我们尝试通过二分图结构对用户-项目交互进行建模，并提出了一种基于位置增强和时间感知图卷积网络 (PTGCN) 的新推荐方法，用于顺序推荐。PTGCN 通过定义位置增强和时间感知图卷积操作并使用自注意力聚合器在二部图上同时学习用户和项目的动态表示，对用户-项目交互之间的序列模式和时间动态进行建模。此外，它通过堆叠多层图卷积来实现用户和项目之间的高阶连接。为了证明 PTGCN 的有效性，我们在三个不同大小的真实世界数据集上对 PTGCN 进行了综合评估，并与一些竞争基线进行了比较。