eprintid: 10194856
rev_number: 7
eprint_status: archive
userid: 699
dir: disk0/10/19/48/56
datestamp: 2024-07-19 12:28:04
lastmod: 2024-07-19 12:28:04
status_changed: 2024-07-19 12:28:04
type: proceedings_section
metadata_visibility: show
sword_depositor: 699
creators_name: Jin, J
creators_name: He, Z
creators_name: Yang, M
creators_name: Zhang, W
creators_name: Yu, Y
creators_name: Wang, J
creators_name: McAuley, J
title: InfoRank: Unbiased Learning-to-Rank via Conditional Mutual Information Minimization
ispublished: pub
divisions: UCL
divisions: B04
divisions: F48
note: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
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.
date: 2024-05-13
date_type: published
publisher: Association for Computing Machinery (ACM)
official_url: http://dx.doi.org/10.1145/3589334.3645356
oa_status: green
full_text_type: other
language: eng
primo: open
primo_central: open_green
verified: verified_manual
elements_id: 2281728
doi: 10.1145/3589334.3645356
isbn_13: 9798400701719
lyricists_name: Wang, Jun
lyricists_id: JWANG00
actors_name: Wang, Jun
actors_id: JWANG00
actors_role: owner
full_text_status: public
pres_type: paper
publication: WWW 2024 - Proceedings of the ACM Web Conference
place_of_pub: Singapore, Singapore
pagerange: 1350-1361
event_title: WWW '24: The ACM Web Conference 2024
book_title: WWW 2024 - Proceedings of the ACM Web Conference
citation:        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.       Green open access   
 
document_url: https://discovery.ucl.ac.uk/id/eprint/10194856/1/2401.12553v1.pdf