Deep Learning for Search
Tommaso Teofili
Description An exploration of neural network-based techniques to improve search relevance and effectiveness. The book discusses the integration of dee...
AI-Powered Search
Trey Grainger, Doug Turnbull, Max Irwin
Book Metadata
Publisher
Simon and Schuster
Duration
14 hr 49 min
ISBN
9781617296970
Genres
Computers
About This Book
Description A technical examination of how to integrate machine learning and natural language processing into search engines to improve relevance and user experience. It covers the transition from traditional keyword-based search to semantic and personalized search. Key Topics: Learning to Rank (LTR), vector search, query expansion, signal processing, and semantic search with embeddings. About the Technology: Modern search platforms (like Solr and Elasticsearch) increasingly rely on AI to understand intent rather than just matching text strings. About the Book: Provides a roadmap for building search pipelines that learn from user behavior and data patterns to deliver more accurate results. About the Reader: For search engineers, data scientists, and developers working with Solr, Elasticsearch, or Lucene-based technologies. About the Author: Trey Grainger and Doug Turnbull are recognized experts in search technology and relevant ranking systems.
AI Summary
AI‑Powered Search is a technical manual that explains how to modernize search pipelines using machine learning, vector embeddings, and relevance‑focused ranking.
It walks readers through moving from keyword‑only matching toward semantic, intent‑aware retrieval for Solr, Elasticsearch, and Lucene environments.
Technical Reference
Summary
AI‑Powered Search is a technical manual that explains how to modernize search pipelines using machine learning, vector embeddings, and relevance‑focused ranking. It walks readers through moving from keyword‑only matching toward semantic, intent‑aware retrieval for Solr, Elasticsearch, and Lucene environments.
Key Themes
- From keyword to semantic ranking
- Learning‑to‑Rank model design and training
- Vector search and dense embeddings
- Query expansion and personalization pipelines
- Operational AI in search (monitoring, latency, evaluation)
Ideal Reader
Search engineers, data scientists, or backend developers who build or maintain Solr, Elasticsearch, or Lucene based systems and need a concrete roadmap for adding AI relevance techniques.
Details
styletechnical yet accessible, with code snippets and pipeline diagrams
levelintermediate‑to‑expert
commitmentdeep dive reference
Why It Matters
The book translates cutting‑edge neural and learning‑to‑rank research into production‑ready patterns for the most common search platforms, giving engineers a clear path from theory to real‑world relevance improvements.
Related Reading
Relevant Search by Trey Grainger & Doug TurnbullElasticsearch: The Definitive Guide – Clinton Gormley & Zachary TongDesigning Data‑Intensive Applications – Martin KleppmannProgramming Solr – Andy McCabe et al.Learning to Rank for Information Retrieval – ChengXiang Zhai
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