Relevant Search

Relevant Search demystifies relevance work. Using Elasticsearch, it teaches you how to return engaging search results to your users, helping you understand and leverage the internals of Lucene-based search engines.

About the Technology

Users are accustomed to and expect instant, relevant search results. To achieve this, you must master the search engine. Yet for many developers, relevance ranking is mysterious or confusing.

About the Book

Relevant Search demystifies the subject and shows you that a search engine is a programmable relevance framework. You’ll learn how to apply Elasticsearch or Solr to your business’s unique ranking problems. The book demonstrates how to program relevance and how to incorporate secondary data sources, taxonomies, text analytics, and personalization. In practice, a relevance framework requires softer skills as well, such as collaborating with stakeholders to discover the right relevance requirements for your business. By the end, you’ll be able to achieve a virtuous cycle of provable, measurable relevance improvements over a search product’s lifetime.

What’s Inside

• Techniques for debugging relevance?
• Applying search engine features to real problems?
• Using the user interface to guide searchers?
• A systematic approach to relevance?
• A business culture focused on improving search

About the Reader

For developers trying to build smarter search with Elasticsearch or Solr.

Table of Contents

1. The search relevance problem
2. Search under the hood
3. Debugging your first relevance problem
4. Taming tokens
5. Basic multifield search
6. Term-centric search
7. Shaping the relevance function
8. Providing relevance feedback
9. Designing a relevance-focused search application
10. The relevance-centered enterprise
11. Semantic and personalized search

About the Author

Doug Turnbull is Staff Relevance Engineer at Spotify and is the former Chief Technical Officer at OpenSource Connections. He is the co-author of the book Relevant Search, and contributed chapters 10-12 on “Learning to Rank”, “Automated Learning to Rank with Click Models”, and “Overcoming Bias in Learned Relevance Models”.

John Berryman is a data scientist at EventBrite where he specializes in recommendations and search. He is interested in the potential of integrating semantic understanding into search and discovery applications.