Search Engine Evaluation based on Search Engine Switching Prediction

Olga Arkhipova, Lidia Grauer, Igor Kuralenok, Pavel Serdyukov

Abstract: In this paper we present a novel application of the search engine switching prediction model for online evaluation. We propose a new metric pSwitch for A/B-testing, which allows us to evaluate the quality of search engines in different aspects such as the quality of the user interface and the quality of the ranking function. pSwitch is a search session-level metric, which relies on the predicted probability that the session contains a switch to another search engine and reflects the degree of the failure of the session. We demonstrate the effectiveness and validity of pSwitch using A/B-testing experiments with real users of search engine Yandex. We compare our metric with recently proposed SpU (sessions per user) metric and other widely used query-level A/B metrics, such as Abandonment Rate and Time to First Click, which we used as our baseline metrics. We observed that pSwitch metric is more sensitive in comparison with those baseline metrics and also that pSwitch and SpU are more consistent with ground truth, than Abandonment Rate and Time to First Click.