AI-Powered Search (Paperback)

Description

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AI-Powered Search teaches you the latest machine learning techniques to create search engines that continuously learn from your users and your content, to drive more domain-aware and intelligent search.

Great search is all about delivering the right results. Today’s search engines are expected to be smart, understanding the nuances of natural language queries, as well as each user’s preferences and context. AI-Powered Search teaches you the latest machine learning techniques to create search engines that continuously learn from your users and your content, to drive more domain-aware and intelligent search. Written by Trey Grainger, the Chief Algorithms Officer at Lucidworks, this authoritative book empowers you to create and deploy search engines that take advantage of user interactions and the hidden semantic relationships in your content to constantly get smarter and automatically deliver better, more relevant search experiences.

Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.

About the technology
The search box has become the de facto user interface for modern data-driven applications. Users expect software to fully understand their search inputs, context, and activity, and to return the right answers every time. Fortunately, you no longer need a massive team manually adjusting relevancy parameters to deliver optimal search results. Using the power of AI, you can develop search solutions that dynamically learn from your content and users, constantly getting smarter and delivering better answers.

About the book
AI-Powered Search is an authoritative guide to applying leading-edge data science techniques to search. It teaches you how to build search engines that automatically understand the intention of a query in order to deliver significantly better results. Author Trey Grainger helped develop numerous algorithms now transforming search, and is an expert on leading techniques for crowdsourced relevancy and semantic search. Working through code in interactive notebooks, you’ll deploy intelligent search systems that deliver real-time personalization and contextual understanding of each user, domain, and query through a self-learning search platform that can tune its own results automatically.

What's inside

• Using reflected intelligence to continually learn and improve search relevancy
• Natural language search with automatically-learned knowledge graphs
• Semantic search with domain-specific terms, phrases, concepts, and relationships
• Personalized search utilizing user behavioral signals and learned user profiles
• Automated Learning to Rank (machine-learned ranking) from user signals
• Word embeddings, vector search, question answering, image and voice search, and other modern search paradigms

About the reader
For software developers or data scientists familiar with the basics of search engine development.

About the author
Trey Grainger is the Chief Algorithms Officer at Lucidworks, the AI-powered search company that powers hundreds of the world’s leading organizations. Trey co-authored Solr in Action and has over 12 years experience building semantic search engines, recommendation engines, real-time analytics systems, and leading related engineering and data science teams.

About the Author

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Trey Grainger is the Chief Algorithms Officer at Lucidworks, the AI-powered search company that powers hundreds of the world’s leading organizations. Trey co-authored Solr in Action and has over 12 years experience building semantic search engines, recommendation engines, real-time analytics systems, and leading related engineering and data science teams.

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”.

Max Irwin is a Managing Consultant at OpenSource Connections, a leading search relevance consultancy. Max contributed chapters 13-14 on “Semantic Search with Dense Vectors” and “Question Answering and the Search Frontier”.