OpenSearchCon India 2025

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 learn:
* How to address the challenges of hybrid search optimization.
* Techniques to predict search parameters using query 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.

After 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.