TY  - GEN
N2  - Most e-commerce product feeds provide blended results of advertised products and recommended products to consumers. The underlying advertising and recommendation platforms share similar if not exactly the same set of candidate products. Consumers' behaviors on the advertised results constitute part of the recommendation model's training data and therefore can influence the recommended results. We refer to this process as Leverage. Considering this mechanism, we propose a novel perspective that advertisers can strategically bid through the advertising platform to optimize their recommended organic traffic. By analyzing the real-world data, we first explain the principles of Leverage mechanism, i.e., the dynamic models of Leverage. Then we introduce a novel Leverage optimization problem and formulate it with a Markov Decision Process. To deal with the sample complexity challenge in model-free reinforcement learning, we propose a novel Hybrid Training Leverage Bidding (HTLB) algorithm which combines the real-world samples and the emulator-generated samples to boost the learning speed and stability. Our offline experiments as well as the results from the online deployment demonstrate the superior performance of our approach.
ID  - discovery10116271
PB  - Association for Computing Machinery (ACM)
UR  - https://doi.org/10.1145/3357384.3357819
CY  - New York, NY, USA
T3  - ACM International Conference on Information and Knowledge Management (CIKM)
A1  - Chen, D
A1  - Jin, J
A1  - Zhang, W
A1  - Pan, F
A1  - Niu, L
A1  - Yu, C
A1  - Wang, J
A1  - Li, H
A1  - Xu, J
A1  - Gai, K
TI  - Learning to Advertise for Organic Traffic Maximization in E-Commerce Product Feeds
SP  - 2527
AV  - public
Y1  - 2019/11/03/
EP  - 2535
N1  - This version is the author accepted manuscript. For information on re-use, please refer to the publisher?s terms and conditions.
ER  -