Context-Aware Query Rewriting for Improving Users' Search Experience on E-commerce Websites

TL;DR

This paper introduces a context-aware query rewriting model for e-commerce search that leverages users' search history to improve query understanding and disambiguation, leading to better search results.

Contribution

The paper presents an end-to-end model that incorporates session graph and attention mechanisms to utilize search context, outperforming existing methods.

Findings

Achieves 11.6% improvement in MRR over baseline

Attains 20.1% higher HIT@16 rate

Demonstrates effectiveness on real-world e-commerce data

Abstract

E-commerce queries are often short and ambiguous. Consequently, query understanding often uses query rewriting to disambiguate user-input queries. While using e-commerce search tools, users tend to enter multiple searches, which we call context, before purchasing. These history searches contain contextual insights about users' true shopping intents. Therefore, modeling such contextual information is critical to a better query rewriting model. However, existing query rewriting models ignore users' history behaviors and consider only the instant search query, which is often a short string offering limited information about the true shopping intent. We propose an end-to-end context-aware query rewriting model to bridge this gap, which takes the search context into account. Specifically, our model builds a session graph using the history search queries and their contained words. We then employ a graph attention mechanism that models cross-query relations and computes contextual information of the session. The model subsequently calculates session representations by combining the contextual information with the instant search query using an aggregation network. The session representations are then decoded to generate rewritten queries. Empirically, we demonstrate the superiority of our method to state-of-the-art approaches under various metrics. On in-house data from an online shopping platform, by introducing contextual information, our model achieves 11.6% improvement under the MRR (Mean Reciprocal Rank) metric and 20.1% improvement under the HIT@16 metric (a hit rate metric), in comparison with the best baseline method (Transformer-based model).