Chen, D;
Jin, J;
Zhang, W;
Pan, F;
Niu, L;
Yu, C;
Wang, J;
... Gai, K; + view all
(2019)
Learning to Advertise for Organic Traffic Maximization in E-Commerce Product Feeds.
In: Zhu, W and Tao, D and Cheng, X and Cui, P and Rundensteiner, E and Carmel, D and He, Q and Yu, JX, (eds.)
CIKM '19: Proceedings of the 28th ACM International Conference on Information and Knowledge Management.
(pp. pp. 2527-2535).
Association for Computing Machinery (ACM): New York, NY, USA.
Preview |
Text
1908.06698.pdf - Accepted Version Download (2MB) | Preview |
Abstract
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.
| Type: | Proceedings paper |
|---|---|
| Title: | Learning to Advertise for Organic Traffic Maximization in E-Commerce Product Feeds |
| Event: | 28th ACM International Conference on Information and Knowledge Management (CIKM '19) |
| Location: | Beijing, PEOPLES R CHINA |
| Dates: | 03 November 2019 - 07 November 2019 |
| ISBN-13: | 9781450369763 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1145/3357384.3357819 |
| Publisher version: | https://doi.org/10.1145/3357384.3357819 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10116271 |
Loading...
Loading...Loading...Loading...Archive Staff Only
 |
View Item |
