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arXiv · 2404.02543

Unbiased Learning to Rank Meets Reality: Lessons from Baidu's Large-Scale Search Dataset

Philipp Hager·Romain Deffayet·Jean-Michel Renders·Onno Zoeter·Maarten de Rijke
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Abstract

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 justified and extensively tested in simulation, ULTR techniques lack empirical validation, especially on modern search engines. The Baidu-ULTR dataset released for the WSDM Cup 2023, collected from Baidu's search engine, offers a rare opportunity to assess the real-world performance of prominent ULTR techniques. Despite multiple submissions during the WSDM Cup 2023 and the subsequent NTCIR ULTRE-2 task, it remains unclear whether the observed improvements stem from applying ULTR or other learning techniques. In this work, we revisit and extend the available experiments on the Baidu-ULTR dataset. We find that standard unbiased learning-to-rank techniques robustly improve click predictions but struggle to consistently improve ranking performance, especially considering the stark differences obtained by choice of ranking loss and query-document features. Our experiments reveal that gains in click prediction do not necessarily translate to enhanced ranking performance on expert relevance annotations, implying that conclusions strongly depend on how success is measured in this benchmark.

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Philipp Hager, Romain Deffayet, Jean-Michel Renders, Onno Zoeter, Maarten de Rijke. 2024-04-03. Unbiased Learning to Rank Meets Reality: Lessons from Baidu's Large-Scale Search Dataset. https://doi.org/10.1145/3626772.3657892

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