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（如在线展示广告）中至关重要。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序相关性，用户行为反映了她对候选项目的兴趣。尽管现有文献已分别考察了这两种相关性，但研究者尚未将它们结合起来进行分析，即语义-时序相关性。我们通过实证方法度量了这种相关性，并观察到了直观且稳健的模式。随后，我们检验了几种流行的用户兴趣模型，发现令人惊讶的是，这些模型均未能很好地学习此类相关性。为填补这一空白，我们提出了一种时序兴趣网络（TIN），以同时捕获行为与目标之间的语义-时序相关性。我们通过在语义编码之外引入目标感知的时序编码，来实现对行为与目标的联合表征。此外，我们通过部署目标感知注意力与目标感知表征来执行显式的四路交互，以同时捕获语义和时序相关性。我们在两个流行的公开数据集上进行了全面评估，所提出的TIN模型在GAUC指标上分别以0.43%和0.29%的优势超越了最佳基线模型。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%的总交易额提升。该模型自2023年10月起已成功部署于生产环境，服务于微信朋友圈流量。我们已在https://github.com/zhouxy1003/TIN 开源相关代码。