Towards Disentangling Relevance and Bias in Unbiased Learning to Rank
Topics: AI (Deep Learning), Marc Najork, Ranking, Scoring, User Signals
The paper is titled “Towards Disentangling Relevance and Bias in Unbiased Learning to Rank” by authors affiliated with various institutions including the University of Illinois and Google Research. The study addresses the challenge of separating relevance and bias within the context of unbiased learning to rank (ULTR). This research is particularly focused on mitigating various biases that arise from implicit user feedback data, such as clicks, which have been a significant area of interest in recent times. The study employs a two-tower architecture approach, which is popular in real-world applications, where click modeling is divided into a relevance tower with regular input features and a bias tower with bias-relevant inputs like the position of a document.
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