  Improved search quality enhances users' satisfaction, which directly impacts
sales growth of an E-Commerce (E-Com) platform. Traditional Learning to Rank
(LTR) algorithms require relevance judgments on products. In E-Com, getting
such judgments poses an immense challenge. In the literature, it is proposed to
employ user feedback (such as clicks, add-to-basket (AtB) clicks and orders) to
generate relevance judgments. It is done in two steps: first, query-product
pair data are aggregated from the logs and then order rate etc are calculated
for each pair in the logs. In this paper, we advocate counterfactual risk
minimization (CRM) approach which circumvents the need of relevance judgements,
data aggregation and is better suited for learning from logged data, i.e.
contextual bandit feedback. Due to unavailability of public E-Com LTR dataset,
we provide \textit{Mercateo dataset} from our platform. It contains more than
10 million AtB click logs and 1 million order logs from a catalogue of about
3.5 million products associated with 3060 queries. To the best of our
knowledge, this is the first work which examines effectiveness of CRM approach
in learning ranking model from real-world logged data. Our empirical evaluation
shows that our CRM approach learns effectively from logged data and beats a
strong baseline ranker ($\lambda$-MART) by a huge margin. Our method
outperforms full-information loss (e.g. cross-entropy) on various deep neural
network models. These findings demonstrate that by adopting CRM approach, E-Com
platforms can get better product search quality compared to full-information
approach. The code and dataset can be accessed at:
https://github.com/ecom-research/CRM-LTR.
