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Unbiased Learning to Rank with Query-Level Click Propensity Estimation: Beyond Pointwise Observation and Relevance
Most existing unbiased learning-to-rank (ULTR) approaches are based on the user
examination hypothesis, which assumes that users will click a result only if it is both relevant …
examination hypothesis, which assumes that users will click a result only if it is both relevant …
Contextual Dual Learning Algorithm with Listwise Distillation for Unbiased Learning to Rank
Unbiased Learning to Rank (ULTR) aims to leverage biased implicit user feedback (eg,
click) to optimize an unbiased ranking model. The effectiveness of the existing ULTR …
click) to optimize an unbiased ranking model. The effectiveness of the existing ULTR …
Understanding the Effects of the Baidu-ULTR Logging Policy on Two-Tower Models
M de Haan, P Hager - arxiv preprint arxiv:2409.12043, 2024 - arxiv.org
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 …
recent work suggests that it suffers from a major limitation that could lead to its collapse in …
Learning From Implicit Feedback for Unbiased Learning to Rank
D Luo - 2025 - search.proquest.com
Search engines serve as one of the most important tools for accessing information online. In
modern search engines, learning to rank~(LTR) algorithms play a critical role by creating …
modern search engines, learning to rank~(LTR) algorithms play a critical role by creating …
데이터 편향이 추천 정확도와 추천결과 편향에 미치는 영향
오소진， 송희석 - Journal of Information Technology Applications & …, 2024 - dbpia.co.kr
To investigate how training data bias impacts recommendation quality, this study
experimented with simulated data to observe changes in recommendation performance and …
experimented with simulated data to observe changes in recommendation performance and …