Jin, J;
He, Z;
Yang, M;
Zhang, W;
Yu, Y;
Wang, J;
McAuley, J;
(2024)
InfoRank: Unbiased Learning-to-Rank via Conditional Mutual Information Minimization.
In:
WWW 2024 - Proceedings of the ACM Web Conference.
(pp. pp. 1350-1361).
Association for Computing Machinery (ACM): Singapore, Singapore.
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Abstract
Ranking items regarding individual user interests is a core technique of multiple downstream tasks such as recommender systems. Learning such a personalized ranker typically relies on the implicit feedback from users' past click-through behaviors. However, collected feedback is biased toward previously highly-ranked items and directly learning from it would result in "rich-get-richer"phenomena. In this paper, we propose a simple yet sufficient unbiased learning-to-rank paradigm named InfoRank that aims to simultaneously address both position and popularity biases. We begin by consolidating the impacts of those biases into a single observation factor, thereby providing a unified approach to addressing bias-related issues. Subsequently, we minimize the mutual information between the observation estimation and the relevance estimation conditioned on the input features. By doing so, our relevance estimation can be proved to be free of bias. To implement InfoRank, we first incorporate an attention mechanism to capture latent correlations within user-item features, thereby generating estimations of observation and relevance. We then introduce a regularization term, grounded in conditional mutual information, to promote conditional independence between relevance estimation and observation estimation. Experimental evaluations conducted across three extensive recommendation and search datasets reveal that InfoRank learns more precise and unbiased ranking strategies.
| Type: | Proceedings paper |
|---|---|
| Title: | InfoRank: Unbiased Learning-to-Rank via Conditional Mutual Information Minimization |
| Event: | WWW '24: The ACM Web Conference 2024 |
| ISBN-13: | 9798400701719 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1145/3589334.3645356 |
| Publisher version: | http://dx.doi.org/10.1145/3589334.3645356 |
| 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 > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10194856 |
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