Estimating Position Bias without Intrusive Interventions

@article{Agarwal2019EstimatingPB,
  title={Estimating Position Bias without Intrusive Interventions},
  author={Aman Agarwal and Ivan Zaitsev and Xuanhui Wang and Cheng Li and Marc Najork and Thorsten Joachims},
  journal={Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining},
  year={2019}
}
  • Aman AgarwalI. Zaitsev T. Joachims
  • Published 12 December 2018
  • Computer Science, Economics
  • Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining
Presentation bias is one of the key challenges when learning from implicit feedback in search engines, as it confounds the relevance signal. [] Key Method First, we show how to harvest a specific type of intervention data from historic feedback logs of multiple different ranking functions, and show that this data is sufficient for consistent propensity estimation in the position-based model. Second, we propose a new extremum estimator that makes effective use of this data. In an empirical evaluation, we find…

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