  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.
