Inverse Propensity Score based offline estimator for deterministic ranking lists using position bias
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Abstract (arXiv)
In this work, we present a novel way of computing IPS using a position-bias model for deterministic logging policies. This technique significantly widens the policies on which OPE can be used. We validate this technique using two different experiments on industry-scale data. The OPE results are clearly strongly correlated with the online results, with some constant bias. The estimator requires the examination model to be a reasonably accurate approximation of real user behaviour.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 6 of its 15 references that are in the map. First posted on arXiv 31 Aug 2022.
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References · 6
- 2016Off-policy evaluation for slate recommendationcited by 102
- 2018Offline A/B testing for Recommender Systemscited by 94
- 2020Off-policy Bandits with Deficient Supportcited by 60
- 2020Counterfactual Evaluation of Slate Recommendations with Sequential Reward Interactionscited by 35
- 2018Offline Evaluation of Ranking Policies with Click Modelscited by 32
- 2017Effective Evaluation using Logged Bandit Feedback from Multiple Loggerscited by 20
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