The importance of lifelong sequential modeling (LSM) is growing in the realm of social media recommendation systems. A key component in this process is the attention module, which derives interest representations with respect to candidate items from the sequence. Typically, attention modules function in a point-wise fashion, concentrating only on the relevance of individual items in the sequence to the candidate item. However, the context information in the neighboring items that is useful for more accurately evaluating the significance of each item has not been taken into account. In this study, we introduce a novel network which employs the Temporal Convolutional Network (TCN) to generate context-aware representations for each item throughout the lifelong sequence. These improved representations are then utilized in the attention module to produce context-aware interest representations. Expanding on this TCN framework, we present a enhancement module which includes multiple TCN layers and their respective attention modules to capture interest representations across different context scopes. Additionally, we also incorporate a lightweight sub-network to create convolution filters based on users' basic profile features. These personalized filters are then applied in the TCN layers instead of the original global filters to produce more user-specific representations. We performed experiments on both a public dataset and a proprietary dataset. The findings indicate that the proposed network surpasses existing methods in terms of prediction accuracy and online performance metrics.

翻译：在社交媒体推荐系统领域，终身序列建模的重要性日益增长。该过程中的一个关键组件是注意力模块，它从序列中推导出相对于候选项目的兴趣表示。通常，注意力模块以逐点方式运作，仅关注序列中单个项目与候选项目的相关性。然而，有助于更准确评估每个项目重要性的邻近项目中的上下文信息尚未被考虑。在本研究中，我们提出了一种新颖的网络，该网络使用时序卷积网络为整个终身序列中的每个项目生成上下文感知的表示。这些改进的表示随后在注意力模块中被用于产生上下文感知的兴趣表示。基于此TCN框架，我们提出了一个增强模块，该模块包含多个TCN层及其相应的注意力模块，以捕捉不同上下文范围内的兴趣表示。此外，我们还引入了一个轻量子网络，基于用户的基本画像特征来创建卷积滤波器。这些个性化滤波器随后被应用于TCN层中，以替代原始的全局滤波器，从而产生更具用户特异性的表示。我们在一个公共数据集和一个专有数据集上进行了实验。结果表明，所提出的网络在预测准确性和在线性能指标方面均超越了现有方法。