Doug Turnbull – författare

　

DESCRIPTION

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 Elasticsearch, Solr, 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 tells how to return engaging search results to users, helping readers understand and leverage the internals of Lucene-based search engines. The book 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. It also shows 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.

 

　

KEY FEATURES

Highly relevant, concrete, hands-on guide

 Digs deep into search engine technology

 Contains essential tools, tips, and strategies for building engaging search engines

 

　

AUDIENCE

For readers who can code moderately complex tasks.

 

ABOUT THE TECHNOLOGY

Lucene is the underlying technology that backs both Elasticsearch and Solr. Dominant search engines are based upon Lucene and since Lucene itself is based upon the strong foundation of Information Retrieval research, the book will be applicable to almost any search technology available now or in the foreseeable future.

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.

Learn statistics by analysing professional basketball data!

Statistics Slam Dunk is an action-packed book that will help you build your skills in exploratory data analysis by digging into the fascinating world of NBA games and player stats using the R language. This textbook will upgrade your R data science skills by taking on practical analysis challenges based on NBA game and player data.

You will take on the challenge of wrangling messy data to drill on the skills that will make you the star player on any data team. And just like in the real world, you will get no clean pre-packaged datasets in this book.

You will develop a toolbox of R data skills including:

Reading and writing data Installing and loading packages Transforming, tidying, and wrangling data Applying best-in-class exploratory data analysis techniques Creating compelling visualizations Developing supervised and unsupervised machine learning algorithms Execute hypothesis tests, including t-tests and chi-square tests for independence Compute expected values, Gini coefficients, and z-scores

Is losing games on purpose a rational strategy? Which hustle statistics have an impact on wins and losses? Each chapter in this one-of-a-kind guide uses new data science techniques to reveal interesting insights like these.

About the technology

Amazing insights are hiding in raw data, and statistical analysis with R can help reveal them! R was built for data, and it supports modelling and statistical techniques including regression and classification models, time series forecasts, and clustering algorithms. And when you want to see your results, R's visualisations are stunning, with best-in-class plots and charts.

Apply cutting-edge machine learning techniques—from crowdsourced relevance and knowledge graph learning, to Large Language Models (LLMs)—to enhance the accuracy and relevance of your search results.Delivering effective search is one of the biggest challenges you can face as an engineer. AI-Powered Search is an in-depth guide to building intelligent search systems you can be proud of. It covers the critical tools you need to automate ongoing relevance improvements within your search applications. Inside you’ll learn modern, data-science-driven search techniques like: • Semantic search using dense vector embeddings from foundation models • Retrieval augmented generation (RAG) • Question answering and summarization combining search and LLMs • Fine-tuning transformer-based LLMs • Personalized search based on user signals and vector embeddings • Collecting user behavioral signals and building signals boosting models • Semantic knowledge graphs for domain-specific learning • Semantic query parsing, query-sense disambiguation, and query intent classification • Implementing machine-learned ranking models (Learning to Rank) • Building click models to automate machine-learned ranking • Generative search, hybrid search, multimodal search, and the search frontier AI-Powered Search will help you build the kind of highly intelligent search applications demanded by modern users. Whether you’re enhancing your existing search engine or building from scratch, you’ll learn how to deliver an AI-powered service that can continuously learn from every content update, user interaction, and the hidden semantic relationships in your content. You’ll learn both how to enhance your AI systems with search and how to integrate large language models (LLMs) and other foundation models to massively accelerate the capabilities of your search technology. Foreword by Grant Ingersoll. About the technology Modern search is more than keyword matching. Much, much more. Search that learns from user interactions, interprets intent, and takes advantage of AI tools like large language models (LLMs) can deliver highly targeted and relevant results. This book shows you how to up your search game using state-of-the-art AI algorithms, techniques, and tools. About the book AI-Powered Search teaches you to create a search that understands natural language and improves automatically the more it is used. As you work through dozens of interesting and relevant examples, you’ll learn powerful AI-based techniques like semantic search on embeddings, question answering powered by LLMs, real-time personalization, and Retrieval Augmented Generation (RAG). What''s inside • Sparse lexical and embedding-based semantic search • Question answering, RAG, and summarization using LLMs • Personalized search and signals boosting models • Learning to Rank, multimodal, and hybrid search About the reader For software developers and data scientists familiar with the basics of search engine technology. About the author Trey Grainger is the Founder of Searchkernel and former Chief Algorithms Officer and SVP of Engineering at Lucidworks. Doug Turnbull is a Principal Engineer at Reddit and former Staff Relevance Engineer at Spotify. Max Irwin is the Founder of Max.io and former Managing Consultant at OpenSource Connections.