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arXiv · 2409.12043

Understanding the Effects of the Baidu-ULTR Logging Policy on Two-Tower Models

Morris de Haan·Philipp Hager
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Abstract

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 collapse in industry applications: the problem of logging policy confounding. Several potential solutions have even been proposed; however, the evaluation of these methods was mostly conducted using semi-synthetic simulation experiments. This paper bridges the gap between theory and practice by investigating the confounding problem on the largest real-world dataset, Baidu-ULTR. Our main contributions are threefold: 1) we show that the conditions for the confounding problem are given on Baidu-ULTR, 2) the confounding problem bears no significant effect on the two-tower model, and 3) we point to a potential mismatch between expert annotations, the golden standard in ULTR, and user click behavior.

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Morris de Haan, Philipp Hager. 2024-09-18. Understanding the Effects of the Baidu-ULTR Logging Policy on Two-Tower Models. https://arxiv.org/abs/2409.12043

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