14 citations · 26 across the 6 of their papers we have counts for
7 papers
CLAX: Fast and Flexible Neural Click Models in JAX
Philipp Hager, Onno Zoeter, Maarten de Rijke
CLAX is a JAX-based library that implements classic click models using modern gradient-based optimization. While neural click models have emerged over the past decade, complex clic…
Unidentified and Confounded? Understanding Two-Tower Models for Unbiased Learning to Rank (Extended Abstract)
Philipp Hager, Onno Zoeter, Maarten de Rijke
Additive two-tower models are popular learning-to-rank methods for handling biased user feedback in industry settings. Recent studies, however, report a concerning phenomenon: trai…
Unidentified and Confounded? Understanding Two-Tower Models for Unbiased Learning to Rank
Philipp Hager, Onno Zoeter, Maarten de Rijke
Additive two-tower models are popular learning-to-rank methods for handling biased user feedback in industry settings. Recent studies, however, report a concerning phenomenon: trai…
Understanding the Effects of the Baidu-ULTR Logging Policy on Two-Tower Models
Morris de Haan, Philipp Hager
Despite the popularity of the two-tower model for unbiased learning to rank (ULTR) tasks, recent work suggests that it suffers from a major limitation that could lead to its collap…
Unbiased Learning to Rank Meets Reality: Lessons from Baidu's Large-Scale Search Dataset
Philipp Hager, Romain Deffayet, Jean-Michel Renders +2
Unbiased learning-to-rank (ULTR) is a well-established framework for learning from user clicks, which are often biased by the ranker collecting the data. While theoretically justif…
Recent Advances in the Foundations and Applications of Unbiased Learning to Rank
Shashank Gupta, Philipp Hager, Jin Huang +2
Since its inception, the field of unbiased learning to rank (ULTR) has remained very active and has seen several impactful advancements in recent years. This tutorial provides both…
An Offline Metric for the Debiasedness of Click Models
Romain Deffayet, Philipp Hager, Jean-Michel Renders +1
A well-known problem when learning from user clicks are inherent biases prevalent in the data, such as position or trust bias. Click models are a common method for extracting infor…