  Improving the performance of click-through rate (CTR) prediction remains one
of the core tasks in online advertising systems. With the rise of deep
learning, CTR prediction models with deep networks remarkably enhance model
capacities. In deep CTR models, exploiting users' historical data is essential
for learning users' behaviors and interests. As existing CTR prediction works
neglect the importance of the temporal signals when embed users' historical
clicking records, we propose a time-aware attention model which explicitly uses
absolute temporal signals for expressing the users' periodic behaviors and
relative temporal signals for expressing the temporal relation between items.
Besides, we propose a regularized adversarial sampling strategy for negative
sampling which eases the classification imbalance of CTR data and can make use
of the strong guidance provided by the observed negative CTR samples. The
adversarial sampling strategy significantly improves the training efficiency,
and can be co-trained with the time-aware attention model seamlessly.
Experiments are conducted on real-world CTR datasets from both in-station and
out-station advertising places.
