Driving Search Intelligence with Query Understanding and Federation

Postman's universal search, introduced in 2020, aimed to facilitate the discovery of various entities like collections and workspaces within the Postman API Platform by implementing a federated search system across multiple Elasticsearch indices. The initial approach categorized results by entities, allowing users to filter search queries; however, a unified result list from a federated search was found to enhance user experience. To address the challenge of differing index-specific scores from Elasticsearch's BM25 algorithm, Postman implemented z-score normalization, allowing for comparable scores across indices. Further improvements included the development of query understanding to predict user intent and boost relevant results based on consumption data and a custom word corpus tailored to Postman’s technical jargon. Machine learning models, particularly SVM with TF-TDF vectorizer, were employed to enhance result relevance, resulting in a significant increase in search conversion rates from 30% to 42% post-implementation. Future enhancements are planned to incorporate semantic search, user-specific interactions, and real-time data replication to continue improving search functionality.

No tracked trend matches for this post yet.

Use this post, company, and trend context to find content marketing opportunities, perform competitive analysis, or address product feature gaps via the Plushcap MCP server or the Plushcap API.