Transforming Search Relevance with Elasticsearch A Case Study in Hybrid and Semantic Search Integration
- Chandan Gaurav
- Nov 9, 2025
- 4 min read
Search relevance shapes how users find what they need quickly and accurately. Our team faced a challenge: improving search accuracy, ranking, and usability on a complex platform with diverse content. We chose Elasticsearch as the foundation but knew that relying on keyword matching alone would not meet user expectations. This blog shares how we transformed search relevance by integrating Elasticsearch with a hybrid search model combining semantic and keyword-based logic.
We will walk through the key steps we took, including structured data indexing, hybrid search implementation, boost value tuning, UX improvements, and feedback-driven iteration. This Elasticsearch case study offers practical insights for product managers, CEOs, and UX leaders looking to improve search relevance and user experience.
Understanding the Search Relevance Challenge
Before our project, the search system relied heavily on keyword matching. Users often received irrelevant or poorly ranked results, especially when queries were ambiguous or used natural language. This led to frustration and lower engagement.
We identified three main issues:
Limited semantic understanding: The system could not grasp user intent beyond exact keyword matches.
Poor ranking: Relevant results were buried under less useful ones due to simplistic scoring.
Suboptimal UX: Search interfaces did not guide users or adapt to their behavior.
Our goal was to improve search relevance by combining semantic search capabilities with traditional keyword search, creating a hybrid search model that balances precision and recall.
Structured Data Indexing For Elasticsearch search relevance
A critical first step was to organize our data for effective search. We implemented structured data indexing in Elasticsearch, which involved:
Defining clear mappings for each content type (e.g., articles, products, courses).
Extracting and normalizing key attributes such as titles, descriptions, tags, and metadata.
Adding semantic annotations using natural language processing (NLP) tools to enrich content with concepts and entities.
This structured approach allowed Elasticsearch to index data more meaningfully, supporting both keyword and semantic queries.
For example, course descriptions were tagged with subject areas and skill levels, enabling more precise filtering and ranking.
Implementing the Hybrid Search Model
We built a hybrid search model that combines keyword matching with semantic search vectors. This approach leverages Elasticsearch’s full-text search capabilities alongside embeddings generated by a semantic model.
Key components included:
Keyword search: Traditional inverted index queries for exact and partial matches.
Semantic search: Vector similarity search using embeddings to capture intent and context.
Query fusion: Combining scores from both methods with adjustable weights.
This hybrid model improved recall by retrieving relevant results even when keywords did not match exactly, while maintaining precision through keyword filters.
Optimizing Boost Values for Search Ranking
Ranking results effectively required fine-tuning Elasticsearch’s scoring parameters. We experimented with boost value optimization to prioritize certain fields and content types.
Our strategy involved:
Assigning higher boosts to titles and tags than to body text.
Increasing weight for recent or popular content.
Applying negative boosts to outdated or low-quality items.
We used A/B testing and user feedback to iteratively adjust these values, resulting in a more intuitive and relevant ranking order.
Enhancing UX for Search Design
Improving semantic search UX was essential to help users interact with the new capabilities. We redesigned the search interface to:
Provide clear feedback on query interpretation.
Offer filters and facets based on structured data.
Suggest related searches and autocomplete options using semantic understanding.
Display result snippets highlighting matched concepts and keywords.
These changes made the search experience more transparent and user-friendly, encouraging exploration and discovery.
Feedback-Driven Iteration and Continuous Improvement
Our work did not stop after launch. We established a feedback loop to monitor search performance and user satisfaction:
Collected search logs and click-through data to identify issues.
Conducted user interviews and usability tests.
Updated semantic models and boost values based on insights.
Released regular improvements to indexing and UX.
This iterative process ensured the search system evolved with user needs and content changes.
Internal Links to Related Case Studies
For readers interested in deeper technical details or related projects, check out this case studies on my site:
Final Thoughts on Search Ranking Optimization
Our experience shows that improving search relevance requires more than just technology. It demands a thoughtful blend of data structuring, hybrid search logic, ranking adjustments, and user-centered design.
By integrating Elasticsearch with semantic search and continuously refining the system based on real user feedback, we achieved a search experience that is both accurate and intuitive.
If you want to improve search relevance on your platform, consider adopting a hybrid approach that balances keyword precision with semantic understanding. This Elasticsearch case study offers a roadmap to building a search system that truly meets user needs.
Comments