Relevant Search – With examples using Elasticsearch and Solr by Doug Turnbull and John Berryman.
From the webpage:
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 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.
- Creating a foundation for Lucene-based search (Solr, Elasticsearch) relevance internals
- Bridging the field of Information Retrieval and real-world search problems
- Building your toolbelt for relevance work
- Solving search ranking problems by combining text analysis, query building, and score shaping
- Providing users relevance feedback so that they can better interact with search
- Integrating test-driven relevance techniques based on A/B testing and content expertise
- Exploring advanced relevance solutions through custom plug-ins and machine learning
Now imagine relevancy searching where a topic map contains multiple subject identifications for a single subject, from different perspectives.
Relevant Search is in early release but the sooner you participate, the fewer errata there will be in the final version.