DESCRIPTION Users expect search to be simple: They enter a few terms and expect perfectly-organized, relevant results instantly. But behind this simple user experience, complex machinery is at work. Whether using Elasticsearch, Solr, or another search technology, the solution is never one size fits all. Returning the right search results requires conveying domain knowledge and business rules in the search engine's data structures, text analytics, and results ranking capabilities. Relevant Search demystifies relevance work. Using Elasticsearch, it tells how to return engaging search results to users, helping readers understand and leverage the internals of Lucene-based search engines. The book walks through several real-world problems using a cohesive philosophy that combines text analysis, query building, and score shaping to express business ranking rules to the search engine. It outlines how to guide the engineering process by monitoring search user behavior and shifting the enterprise to a search-first culture focused on humans, not computers. It also shows how the search engine provides a deeply pluggable platform for integrating search ranking with machine learning, ontologies, personalization, domain-specific expertise, and other enriching sources.   KEY FEATURES Highly relevant, concrete, hands-on guide  Digs deep into search engine technology  Contains essential tools, tips, and strategies for building engaging search engines   AUDIENCE For readers who can code moderately complex tasks.   ABOUT THE TECHNOLOGY Lucene is the underlying technology that backs both Elasticsearch and Solr. Dominant search engines are based upon Lucene and since Lucene itself is based upon the strong foundation of Information Retrieval research, the book will be applicable to almost any search technology available now or in the foreseeable future.

Doug Turnbull is a search relevance consultant at OpenSource Connections where he frequently speaks and blogs. Using Solr and Elasticsearch, Doug builds relevant, semantically enriched search experiences for clients across multiple domains. 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.