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User interaction data is an important source of supervision in counterfactuallearning to rank (CLTR). Such data suffers from presentation bias. Much work inunbiased learning to rank (ULTR) focuses on position bias, i.e., items athigher ranks are more likely to be examined and clicked. Inter-itemdependencies also influence examination probabilities, with outlier items in aranking as an important example. Outliers are defined as items that observablydeviate from the rest and therefore stand out in the ranking. In this paper, weidentify and introduce the bias brought about by outlier items: users tend toclick more on outlier items and their close neighbors. To this end, we firstconduct a controlled experiment to study the effect of outliers on user clicks.Next, to examine whether the findings from our controlled experiment generalizeto naturalistic situations, we explore real-world click logs from an e-commerceplatform. We show that, in both scenarios, users tend to click significantlymore on outlier items than on non-outlier items in the same rankings. We showthat this tendency holds for all positions, i.e., for any specific position, anitem receives more interactions when presented as an outlier as opposed to anon-outlier item. We conclude from our analysis that the effect of outliers onclicks is a type of bias that should be addressed in ULTR. We therefore proposean outlier-aware click model that accounts for both outlier and position bias,called outlier-aware position-based model ( OPBM). We estimate clickpropensities based on OPBM ; through extensive experiments performed on bothreal-world e-commerce data and semi-synthetic data, we verify the effectivenessof our outlier-aware click model. Our results show the superiority of OPBMagainst baselines in terms of ranking performance and true relevanceestimation.

On the Impact of Outlier Bias on User Clicks