ScaDS.AI announces and welcomes you to join our public colloquium session on Friday, May 26, 2023 at 11:00-12:00 CEST. Exceptionally, the colloquium takes place purely online (link to Zoom session) and will be recorded.
Assistant Professor, Data Science Group of the Institute of Computing and Information Sciences (iCIS), Radboud University, Nijmegen, The Netherlands
From Inverse-Propensity-Scoring to Doubly Robust Estimation: Counterfactual Learning to Rank for Search and Recommendation
Search and recommendation systems are vital for the accessibility of content on the internet. The basis for these systems are ranking models that turn collections of items into rankings: small, ordered lists of items to be displayed to users. Modern ranking models are mostly optimized based on user interactions. Generally, learning from user behavior leads to systems that receive more user engagement than those optimized based on expert judgements. However, user interactions are biased indicators of user preference: often whether something is interacted has less to do with preference and more with where and how it was presented.
In response to this bias problem, recent years have seen the introduction and development of the counterfactual Learning to Rank (LTR) field. This field covers methods that learn from historical user interactions, i.e. click logs, and aim to optimize ranking models w.r.t. the actual user preferences. In order to achieve this goal, counterfactual LTR methods have to correct the biases that affect clicks.In this talk I will present a recently introduced doubly robust method for correcting position-bias in user interactions with rankings.
Dr. Harrie Oosterhuis
- Pursues research on the intersection of Machine Learning and Information Retrieval, primarily concerning learning from user interactions on rankings
- PhD in Machine Learning and Information Retrieval at University of Amsterdam, The Netherlands, in 2020, promoted by Maarten de Rijke
- 2021 Google Research Scholar Award for early career researchers
Location
Online only via a Zoom session.
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