Bayesian Ranker Comparison Based on Historical User Interactions

Abstract

We address the problem of how to safely compare rankers for information retrieval. In particular, we consider how to control the risks associated with switching from an existing production ranker to a new candidate ranker. Whereas existing online comparison methods require showing potentially suboptimal result lists to users during the comparison process, which can lead to user frustration and abandonment, our approach only requires user interaction data generated through the natural use of the production ranker. Specifically, we propose a Bayesian approach for (1) comparing the production ranker to candidate rankers and (2) estimating the confidence of this comparison. The comparison of rankers is performed using click model-based information retrieval metrics, while the confidence of the comparison is derived from Bayesian estimates of uncertainty in the underlying click model. These confidence estimates are then used to determine whether a risk-averse decision criterion for switching to the candidate ranker has been satisfied. Experimental results on several learning to rank datasets and on a click log show that the proposed approach outperforms an existing ranker comparison method that does not take uncertainty into account.