Structured Query Reformulations in Commerce Search

TL;DR

This paper presents a method to improve commerce search by reformulating user queries with modifiers into attribute-specific queries using user behavior and structured data, leading to more relevant product retrieval.

Contribution

The study introduces a novel approach to automatically rewrite modifier terms in queries into attribute values, enhancing search relevance in commerce platforms.

Findings

Users agree with reformulated attribute values in 95% of cases.

Reformulated queries yield better user preferences in 87% of cases.

The approach effectively bridges query semantics and product attributes.

Abstract

Recent work in commerce search has shown that understanding the semantics in user queries enables more effective query analysis and retrieval of relevant products. However, due to lack of sufficient domain knowledge, user queries often include terms that cannot be mapped directly to any product attribute. For example, a user looking for {\tt designer handbags} might start with such a query because she is not familiar with the manufacturers, the price ranges, and/or the material that gives a handbag designer appeal. Current commerce search engines treat terms such as {\tt designer} as keywords and attempt to match them to contents such as product reviews and product descriptions, often resulting in poor user experience. In this study, we propose to address this problem by reformulating queries involving terms such as {\tt designer}, which we call \emph{modifiers}, to queries that specify precise product attributes. We learn to rewrite the modifiers to attribute values by analyzing user behavior and leveraging structured data sources such as the product catalog that serves the queries. We first produce a probabilistic mapping between the modifiers and attribute values based on user behavioral data. These initial associations are then used to retrieve products from the catalog, over which we infer sets of attribute values that best describe the semantics of the modifiers. We evaluate the effectiveness of our approach based on a comprehensive Mechanical Turk study. We find that users agree with the attribute values selected by our approach in about 95% of the cases and they prefer the results surfaced for our reformulated queries to ones for the original queries in 87% of the time.