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arXiv · 1911.00886

Regularized Adversarial Sampling and Deep Time-aware Attention for Click-Through Rate Prediction

Yikai Wang·Liang Zhang·Quanyu Dai·Fuchun Sun·Bo Zhang·Yang He·Weipeng Yan·Yongjun Bao
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Abstract

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.

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Yikai Wang, Liang Zhang, Quanyu Dai, Fuchun Sun, Bo Zhang, Yang He, Weipeng Yan, Yongjun Bao. 2019-11-03. Regularized Adversarial Sampling and Deep Time-aware Attention for Click-Through Rate Prediction. https://doi.org/10.1145/3357384.3357936

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