computers > internet / search engines
AI-Powered Search
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Imported Edition - Ships in 10-12 Days
Free Shipping in India on orders above Rs. 500
From the Back Cover
Great search is all about delivering the right results. Today's search engines are expected to be smart and understand the nuances of natural language queries, as well as each user's preferences and context.
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 is an expert on leading techniques for crowdsourced relevancy and semantic search and has helped develop numerous algorithms which have transformed search. Working through code in interactive notebooks, you will deploy intelligent search systems that deliver real-time personalisation and contextual understanding of each user, domain, and query through a self-learning search platform that can tune its own results automatically.
About the reader
For software developers or data scientists familiar with the basics of search engine development.
Grainger, Trey: - 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.
Irwin, Max: - 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".
Turnbull, Doug: - 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".
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