1 citations · 1 across the 10 of their papers we have counts for
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Can LLM Annotations Replace User Clicks for Learning to Rank?
Lulu Yu, Keping Bi, Jiafeng Guo +4
Large-scale supervised data is essential for training modern ranking models, but obtaining high-quality human annotations is costly. Click data has been widely used as a low-cost a…
Unbiased Learning to Rank with Query-Level Click Propensity Estimation: Beyond Pointwise Observation and Relevance
Lulu Yu, Keping Bi, Jiafeng Guo +3
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 and…
Contextual Dual Learning Algorithm with Listwise Distillation for Unbiased Learning to Rank
Lulu Yu, Keping Bi, Shiyu Ni +1
Unbiased Learning to Rank (ULTR) aims to leverage biased implicit user feedback (e.g., click) to optimize an unbiased ranking model. The effectiveness of the existing ULTR methods…
CIR at the NTCIR-17 ULTRE-2 Task
Lulu Yu, Keping Bi, Jiafeng Guo +1
The Chinese academy of sciences Information Retrieval team (CIR) has participated in the NTCIR-17 ULTRE-2 task. This paper describes our approaches and reports our results on the U…
Ensemble Ranking Model with Multiple Pretraining Strategies for Web Search
Xiaojie Sun, Lulu Yu, Yiting Wang +2
An effective ranking model usually requires a large amount of training data to learn the relevance between documents and queries. User clicks are often used as training data since…
Feature-Enhanced Network with Hybrid Debiasing Strategies for Unbiased Learning to Rank
Lulu Yu, Yiting Wang, Xiaojie Sun +2
Unbiased learning to rank (ULTR) aims to mitigate various biases existing in user clicks, such as position bias, trust bias, presentation bias, and learn an effective ranker. In th…