Whether using Solr, Elasticsearch, 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 teaches you how to return engaging search results to your users, helping you understand and leverage the internals of Lucene-based search engines. Relevant Search 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. You'll see 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.
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