Position Bias Estimation for Unbiased Learning to Rank in Personal Search

  title={Position Bias Estimation for Unbiased Learning to Rank in Personal Search},
  author={Xuanhui Wang and Nadav Golbandi and Michael Bendersky and Donald Metzler and Marc Najork},
A well-known challenge in learning from click data is its inherent bias and most notably position bias. Traditional click models aim to extract the ‹query, document› relevance and the estimated bias is usually discarded after relevance is extracted. In contrast, the most recent work on unbiased learning-to-rank can effectively leverage the bias and thus focuses on estimating bias rather than relevance [20, 31]. Existing approaches use search result randomization over a small percentage of… CONTINUE READING
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