Counterfactual Learning from Logs for Improved Ranking of E-Commerce Products

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 (add-to-basket (AtB) clicks, orders etc) 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{Commercial 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 CRM approach learns effectively from logged data and beats 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 show that by adopting CRM approach, E-Com platforms can improve product search quality without artificially mending the data to fit in the traditional LTR algorithms.