User response prediction is essential in industrial recommendation systems, such as online display advertising. Among all the features in recommendation models, user behaviors are among the most critical. Many works have revealed that a user's behavior reflects her interest in the candidate item, owing to the semantic or temporal correlation between behaviors and the candidate. While the literature has individually examined each of these correlations, researchers have yet to analyze them in combination, that is, the semantic-temporal correlation. We empirically measure this correlation and observe intuitive yet robust patterns. We then examine several popular user interest models and find that, surprisingly, none of them learn such correlation well. To fill this gap, we propose a Temporal Interest Network (TIN) to capture the semantic-temporal correlation simultaneously between behaviors and the target. We achieve this by incorporating target-aware temporal encoding, in addition to semantic encoding, to represent behaviors and the target. Furthermore, we conduct explicit 4-way interaction by deploying target-aware attention and target-aware representation to capture both semantic and temporal correlation. We conduct comprehensive evaluations on two popular public datasets, and our proposed TIN outperforms the best-performing baselines by 0.43% and 0.29% on GAUC, respectively. During online A/B testing in Tencent's advertising platform, TIN achieves 1.65% cost lift and 1.93% GMV lift over the base model. It has been successfully deployed in production since October 2023, serving the WeChat Moments traffic. We have released our code at https://github.com/zhouxy1003/TIN.

翻译：用户响应预测在工业推荐系统中至关重要，例如在线展示广告。在推荐模型的所有特征中，用户行为是最关键的特征之一。许多研究表明，由于行为与候选项目之间存在语义或时间相关性，用户行为能反映其对候选项目的兴趣。虽然现有文献单独研究了这些相关性，但尚未对其组合分析，即语义-时间相关性。我们通过实验测量了这种相关性，并观察到直观且稳健的模式。接着，我们考察了几种流行的用户兴趣模型，并惊讶地发现，它们均未能很好地学习这种相关性。为弥补这一空白，我们提出了时间兴趣网络（Temporal Interest Network，TIN），以同时捕捉行为与目标之间的语义-时间相关性。我们通过引入目标感知的时间编码（与语义编码结合）来表示行为与目标，从而实现这一目标。此外，我们通过部署目标感知注意力与目标感知表示，执行显式的四向交互，以同时捕捉语义与时间相关性。我们在两个流行的公开数据集上进行了全面评估，提出的TIN在GAUC指标上分别以0.43%和0.29%的优势超越最佳基线模型。在腾讯广告平台的在线A/B测试中，TIN相较于基线模型实现了1.65%的成本提升和1.93%的GMV提升。自2023年10月起，该模型已成功部署于生产环境，服务于微信朋友圈流量。代码已开源至https://github.com/zhouxy1003/TIN。