Dai, X;
Lin, J;
Zhang, W;
Li, S;
Liu, W;
Tang, R;
He, X;
... Yu, Y; + view all
(2021)
An adversarial imitation click model for information retrieval.
In:
WWW '21: Proceedings of the Web Conference 2021.
(pp. pp. 1809-1820).
Association for Computing Machinery
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Abstract
Modern information retrieval systems, including web search, ads placement, and recommender systems, typically rely on learning from user feedback. Click models, which study how users interact with a ranked list of items, provide a useful understanding of user feedback for learning ranking models. Constructing "right"dependencies is the key of any successful click model. However, probabilistic graphical models (PGMs) have to rely on manually assigned dependencies, and oversimplify user behaviors. Existing neural network based methods promote PGMs by enhancing the expressive ability and allowing flexible dependencies, but still suffer from exposure bias and inferior estimation. In this paper, we propose a novel framework, Adversarial Imitation Click Model (AICM), based on imitation learning. Firstly, we explicitly learn the reward function that recovers users' intrinsic utility and underlying intentions. Secondly, we model user interactions with a ranked list as a dynamic system instead of one-step click prediction, alleviating the exposure bias problem. Finally, we minimize the JS divergence through adversarial training and learn a stable distribution of click sequences, which makes AICM generalize well across different distributions of ranked lists. A theoretical analysis has indicated that AICM reduces the exposure bias from O(T2) to O(T). Our studies on a public web search dataset show that AICM not only outperforms state-of-the-art models in traditional click metrics but also achieves superior performance in addressing the exposure bias and recovering the underlying patterns of click sequences.
| Type: | Proceedings paper |
|---|---|
| Title: | An adversarial imitation click model for information retrieval |
| Event: | Web Conference 2021 |
| ISBN-13: | 9781450383127 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1145/3442381.3449913 |
| Publisher version: | https://doi.org/10.1145/3442381.3449913 |
| Language: | English |
| Additional information: | This paper is published under the Creative Commons Attribution 4.0 International (CC-BY 4.0) license. Authors reserve their rights to disseminate the work on their personal and corporate Web sites with the appropriate attribution. |
| 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/10130380 |
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