Predicting Search Satisfaction Metrics with Interleaved Comparisons

The gold standard for online retrieval evaluation is AB testing. Rooted in the idea of a controlled experiment, AB tests compare the performance of an experimental system (treatment) on one sample of the user population, to that of a baseline system (control) on another sample. Given an online evaluation metric that accurately reflects user satisfaction, these tests enjoy high validity. However, due to the high variance across users, these comparisons often have low sensitivity, requiring millions of queries to detect statistically significant differences between systems. Interleaving is an alternative online evaluation approach, where each user is presented with a combination of results from both the control and treatment systems. Compared to AB tests, interleaving has been shown to be substantially more sensitive. However, interleaving methods have so far focused on user clicks only, and lack support for more sophisticated user satisfaction metrics as used in AB testing. In this paper we present the first method for integrating user satisfaction metrics with interleaving. We show how interleaving can be extended to (1) directly match user signals and parameters of AB metrics, and (2) how parameterized interleaving credit functions can be automatically calibrated to predict AB outcomes. We also develop a new method for estimating the relative sensitivity of interleaving and AB metrics, and show that our interleaving credit functions improve agreement with AB metrics without sacrificing sensitivity. Our results, using 38 large-scale online experiments en- compassing over 3 billion clicks in a web search setting, demonstrate up to a 22% improvement in agreement with AB metrics (constituting over a 50% error reduction), while maintaining sensitivity of one to two orders of magnitude above the AB tests. This paves the way towards more sensitive and accurate online evaluation.