Paper Info
Title
Regularized Adversarial Sampling and Deep Time-aware Attention for Click-Through Rate Prediction
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.
Year
DOI
Venue
2019
10.1145/3357384.3357936
Proceedings of the 28th ACM International Conference on Information and Knowledge Management
Keywords
Field
DocType
adversarial sampling, ctr prediction, time-aware attention
Data mining,Click-through rate,Computer science,Attention model,Online advertising,Sampling (statistics),Deep time,Artificial intelligence,Predictive modelling,Deep learning,Machine learning,Adversarial system
Conference
ISBN
Citations 
PageRank 
978-1-4503-6976-3
4
0.40
References 
Authors
0
8
Name
Order
Citations
PageRank
Yikai Wang152.44
Liang Zhang217611.95
Quanyu Dai3285.28
Fuchun Sun42377225.80
Bo Zhang52724191.41
Yang He651.08
Weipeng Yan773.14
Yongjun Bao81359.32