The history of user behaviors constitutes one of the most significant
characteristics in predicting the click-through rate (CTR), owing to their
strong semantic and temporal correlation with the target item. While the
literature has individually examined each of these correlations, research has
yet to analyze them in combination, that is, the quadruple correlation of
(behavior semantics, target semantics, behavior temporal, and target temporal).
The effect of this correlation on performance and the extent to which existing
methods learn it remain unknown. To address this gap, we empirically measure
the quadruple correlation and observe intuitive yet robust quadruple patterns.
We measure the learned correlation of several representative user behavior
methods, but to our surprise, none of them learn such a pattern, especially the
temporal one.
In this paper, we propose the Temporal Interest Network (TIN) to capture the
quadruple semantic and temporal correlation between behaviors and the target.
We achieve this by incorporating target-aware temporal encoding, in addition to
semantic embedding, to represent behaviors and the target. Furthermore, we
deploy target-aware attention, along with target-aware representation, to
explicitly conduct the 4-way interaction. We performed comprehensive
evaluations on the Amazon and Alibaba datasets. Our proposed TIN outperforms
the best-performing baselines by 0.43\% and 0.29\% on two datasets,
respectively. Comprehensive analysis and visualization show that TIN is indeed
capable of learning the quadruple correlation effectively, while all existing
methods fail to do so. We provide our implementation of TIN in Tensorflow.

通过分析和评估用户行为与目标之间的四重语义和时间相关性，本文提出了一种名为 Temporal Interest Network (TIN) 的模型，并证明它能有效地学习这种四重相关性，从而显著提高了性能。