Query expansion based on user behaviour

Summary

Grab developed a query expansion framework to resolve search issues such as typos, multi-language terms, missing alternatives, and word spacing. The corpus is generated through manual annotation for the top thousand fat-head queries and session-based data mining of user rewrite patterns. Offline pipelines extract adjacent query rewrites occurring within 30 seconds where only the second query generated a click, limiting candidates to three per query for serving efficiency. In production, expanded keywords pass through a query understanding, multi-recall, and result fusion flow with automated pruning of underperforming pairs. Online A/B testing across six Southeast Asian countries showed improvements in click-through and conversion rates, with data-mined expansions outperforming manual annotations alone.

Context

Grab users encountered suboptimal search results caused by typos, synonyms, spacing issues, multi-language queries, and queries returning non-operational or out-of-radius merchants without alternatives.

Approach / What changed

Grab built a query expansion pipeline combining manual annotations for the top 1,000 queries with data mining on session rewrite logs, serving up to three expansion candidates through a multi-recall and result fusion architecture.

Takeaways

• User rewrite mining heuristics extracted adjacent query pairs within 30-second windows where the initial query had no click and the second query received a click.
• Expansion candidates were capped at three per query to maintain serving efficiency across query understanding, multi-recall, and result fusion stages.
• A/B testing across six countries demonstrated that data-mined query expansions produced higher click-through and conversion rate uplifts than manual annotations alone.

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