Query expansion based on user behaviour

This post details the implementation of a query expansion framework for Grab's search system. It describes two methods for generating expansion candidates: manual annotation of top keywords and data mining based on user rewrites. The data mining approach leverages user search session data to identify effective rewrites based on heuristics like adjacent queries, click behavior, time intervals, and frequency. The post also outlines the expansion matching architecture within the existing search system and the continuous monitoring and feedback loop for maintaining relevance. Results from A/B experiments show an uplift in CTR and conversion rate, with the data mining approach performing better.