Evaluating and Improving Search Relevancy with a Confusion Matrix

Staff Reporter

Written by Murray Woodman and published by Morpht, the article outlines a practical framework for evaluating and improving search result relevancy using a confusion matrix. It explains that relevancy goes beyond keyword matching and includes factors such as user behaviour, semantic meaning, time, popularity, and context. The article categorises search outcomes as true positives, false positives, false negatives, and true negatives, focusing on reducing false negatives to improve user experience. Measurement is based on recall, and assessment is limited to the first page of search results due to user behaviour.

The piece recommends defining typical search queries with the site owner, establishing expected results, and running evaluations to measure recall. Iterative testing and configuration adjustments help identify patterns among false negatives and refine ranking systems. Emphasis is placed on broad improvements rather than narrow optimisation for specific queries.

Technologies such as Solr and the Drupal Search API are highlighted for their ability to support relevancy tuning through content indexing, boosting, and fuzzy search techniques. Adjustments to boost HTML elements, content types, or recency can enhance the relevance of returned results within the technical limits of the platform.

Beyond keyword matching

Looking forward, Woodman notes that search complexity continues to grow. Traditional keyword-density methods are being enhanced with semantic and behavioural approaches. Platforms like Algolia and Recombee demonstrate how embedding-based semantic search and user behaviour data can personalise and improve relevancy across contexts.

“The measurement of results can lead to insights to drive better configuration of the technology to improve outcomes for users across a wide range of scenarios,”

Woodman concludes.

Reference: Improving search relevancy with a confusion matrix by Murray Woodman (murrayw), Morpht (8 October 2025)

Morpht

Search API

Solr

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