Peng, Z;
Jin, J;
Luo, L;
Yang, Y;
Luo, R;
Wang, J;
Zhang, W;
... Gai, K; + view all
(2020)
Learning to Infer User Hidden States for Online Sequential Advertising.
In: D'Aquin, M and Dietze, S and Hauff, C and Curry, E and Cudre Mauroux, P, (eds.)
CIKM '20: Proceedings of the 29th ACM International Conference on Information & Knowledge Management.
(pp. pp. 2677-2684).
Association for Computing Machinery (ACM): New York, NY, USA.
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Abstract
To drive purchase in online advertising, it is of the advertiser's great interest to optimize the sequential advertising strategy whose performance and interpretability are both important. The lack of interpretability in existing deep reinforcement learning methods makes it not easy to understand, diagnose and further optimize the strategy.In this paper, we propose our Deep Intents Sequential Advertising (DISA) method to address these issues. The key part of interpretability is to understand a consumer's purchase intent which is, however, unobservable (called hidden states). In this paper, we model this intention as a latent variable and formulate the problem as a Partially Observable Markov Decision Process (POMDP) where the underlying intents are inferred based on the observable behaviors. Large-scale industrial offline and online experiments demonstrate our method's superior performance over several baselines. The inferred hidden states are analyzed, and the results prove the rationality of our inference.
| Type: | Proceedings paper |
|---|---|
| Title: | Learning to Infer User Hidden States for Online Sequential Advertising |
| Event: | 29th ACM International Conference on Information & Knowledge Management (CIKM '20) |
| ISBN-13: | 9781450368599 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1145/3340531.3412721 |
| Publisher version: | https://doi.org/10.1145/3340531.3412721 |
| 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/10116268 |
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