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

This book is ideal for software developers or data scientists familiar with the basics of search engine development. It will show you ways to create content that will constantly get smarter and automatically deliver better, more relevant search experiences. What’s inside

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
Personalised search, utilising user behavioural 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 technology The search box has become the “de facto” user interface for modern data-driven applications. Users expect the software to fully understand their search inputs, context, and activity and 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.

Author Biography

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 of experience building semantic search engines, recommendation engines, and real-time analytics systems, and leading related engineering and data science teams.

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