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Found 4 papers, 3 papers with code
Unbiased Learning to Rank Meets Reality: Lessons from Baidu's Large-Scale Search Dataset
However, these gains in click prediction do not translate to enhanced ranking performance on expert relevance annotations, implying that conclusions strongly depend on how success is measured in this benchmark.
Recent Advances in the Foundations and Applications of Unbiased Learning to Rank
This tutorial provides both an introduction to the core concepts of the field and an overview of recent advancements in its foundations along with several applications of its methods.
An Offline Metric for the Debiasedness of Click Models
We prove that debiasedness is a necessary condition for recovering unbiased and consistent relevance scores and for the invariance of click prediction under covariate shift.
Multifaceted Domain-Specific Document Embeddings
Current document embeddings require large training corpora but fail to learn high-quality representations when confronted with a small number of domain-specific documents and rare terms.
