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Relevant Search: With applications for Solr and Elasticsearch

Book Description

Summary

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. Using Elasticsearch and Solr, it teaches you to express your business’s ranking rules in this framework. You’ll discover 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.

    About the Authors

    Doug Turnbull is lead relevance consultant at OpenSource Connections, where he frequently speaks and blogs. John Berryman is a data engineer at Eventbrite, where he specializes in recommendations and search.