Multifaceted Reformulations for Null & Low queries and its parallelism with Counterfactuals

Search engines are crucial in retrieving relevant items based on user-specified queries. A significant challenge arises when the buyer's vocabulary does not align with that of the seller, leading to a lack of sufficient recall or unsat-isfactory results. Such queries are referred to as “Null and Low” (N&L) queries which greatly hinder the overall user experience. Moreover, through analysis of user search behavioral data from a major e-commerce company, we have identified that approximately 29% of search queries exhibit multiple category interpretations, which we call “multi-faceted query interpretations”. In this study, we provide conceptual parallelism between the problem of N&L query reformulation and counterfactual explanation literature. To enhance the user experience for N&L queries, we propose a novel method that leverages the capabilities of a neural translation model to provide diverse and multiple reformulations. The proposed model demonstrated exceptional performance in our experiments, achieving an impressive 10% F-score improvement on the held-out test dataset with 5% improvement in relevance and a 100% increase in recall set size compared to a heuristic baseline, specifically for a set of N&L queries sampled from user traffic in eBay. By addressing the challenges of N&L queries and enabling the generation of diverse reformulations, our approach significantly enhances the overall search experience for users.