Wang, W;
Jin, J;
Hao, J;
Chen, C;
Yu, C;
Zhang, W;
Wang, J;
... Gai, K; + view all
(2019)
Learning Adaptive Display Exposure for Real-Time Advertising.
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. 2595-2603).
Association for Computing Machinery (ACM): New York, NY, USA.
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Abstract
In E-commerce advertising, where product recommendations and product ads are presented to users simultaneously, the traditional setting is to display ads at fixed positions. However, under such a setting, the advertising system loses the flexibility to control the number and positions of ads, resulting in sub-optimal platform revenue and user experience. Consequently, major e-commerce platforms (e.g., Taobao.com) have begun to consider more flexible ways to display ads. In this paper, we investigate the problem of advertising with adaptive exposure: can we dynamically determine the number and positions of ads for each user visit under certain business constraints so that the platform revenue can be increased? More specifically, we consider two types of constraints: request-level constraint ensures user experience for each user visit, and platform-level constraint controls the overall platform monetization rate. We model this problem as a Constrained Markov Decision Process with per-state constraint (psCMDP) and propose a constrained two-level reinforcement learning approach to decompose the original problem into two relatively independent sub-problems. To accelerate policy learning, we also devise a constrained hindsight experience replay mechanism. Experimental evaluations on industry-scale real-world datasets demonstrate the merits of our approach in both obtaining higher revenue under the constraints and the effectiveness of the constrained hindsight experience replay mechanism.
| Type: | Proceedings paper |
|---|---|
| Title: | Learning Adaptive Display Exposure for Real-Time Advertising |
| 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: | 10.1145/3357384.3357806 |
| Publisher version: | https://doi.org/10.1145/3357384.3357806 |
| 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/10116272 |
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