From Static to Dynamic: Data-Driven Query Understanding to Supercharge Hybrid Search

“As AI-powered search evolves, hybrid search has become a powerful method for enhancing result quality. However, tuning hybrid search is a complex optimization problem due to the need to balance lexical and semantic relevance while adapting to diverse query intents.

In this talk, we’ll present a scalable, data-driven framework for optimizing hybrid search using machine learning techniques, focusing on real-time query adaptation to maximize relevance. We’ll explore how we transitioned from a static one-size-fits-all approach to query-specific optimization, using machine learning to dynamically adjust search parameters. Attendees will gain insights into:

• How to address the challenges of hybrid search optimization.
• Techniques to predict and fine-tune search parameters using query-specific and query result-specific signals.
• Improvements in search quality metrics (DCG, NDCG, Precision) of more than 7% compared to the static approach.
• Steps to integrate query-specific optimization into your existing search infrastructure.

By the end of this session, attendees will be equipped to build adaptable, machine learning-driven hybrid search systems that respond dynamically to user queries, resulting in measurable gains in search relevance and user satisfaction.”

Daniel Wrigley

OpenSource Connections LLC

Daniel enjoys building powerful search stacks to deliver relevant search as a consultant at OpenSource Connections. He has worked in search since graduating in computational linguistics studies at Ludwig-Maximilians-University Munich in 2012 where he developed his weakness for search and natural language processing. His experience as a search consultant paved the way for becoming an O’Reilly author co-authoring the first German book on Apache Solr. In his free time he supports the local fire brigade as a volunteer firefighter and serves as the sports director of the local shooting club in the village he lives in.