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In this paper, we address the problem of evaluating whether results served byan e-commerce search engine for a query are good or not. This is a criticalquestion in evaluating any e-commerce search engine. While this question istraditionally answered using simple metrics like query click-through rate(CTR), we observe that in e-commerce search, such metrics can be misleading.Upon inspection, we find cases where CTR is high but the results are poor andvice versa. Similar cases exist for other metrics like time to click which areoften also used for evaluating search engines. We aim to learn the quality ofthe results served by the search engine based on users' interactions with theresults. Although this problem has been studied in the web search context, thisis the first study for e-commerce search, to the best of our knowledge. Despitecertain commonalities with evaluating web search engines, there are severalmajor differences such as underlying reasons for search failure, andavailability of rich user interaction data with products (e.g. adding a productto the cart). We study large-scale user interaction logs from Flipkart's searchengine, analyze behavioral patterns and build models to classify queries basedon user behavior signals. We demonstrate the feasibility and efficacy of suchmodels in accurately predicting query performance. Our classifier is able toachieve an average AUC of 0.75 on a held-out test set.

Did We Get It Right? Predicting Query Performance in E-commerce Search