More Sensitive Suggestions

From Deep Learning for Search by Tommaso Teofili

This article discusses how neural networks can help generate text that a human might write in order to provide more sensitive suggestions and enhance autocomplete functionality.

Build a Full-Featured Data Solution


From Fusion in Action by Guy Sperry

What does Deep Learning Contribute to Search

From Deep Learning for Search by Tommaso Teofili

If you’ve ever worked on designing, implementing or configuring a search engine, you’ve faced the problem of having a solution that adapts to your data; deep learning helps provide solutions to these problems which are accurately based on the data, not on fixed rules or algorithms.

Relevant Search: Debugging Query Matching

From Relevant Search by Doug Turnbull and John Berryman

This article discusses debugging query matching in search engines.

Elasticsearch Percolator: search turned upside down

From Elasticsearch in Action
Elasticsearch Percolator: search turned upside down

Elasticsearch Index Scaling Out and Back In with Default Settings

From Elasticsearch in Action
Elasticsearch index scaling out and back in with default settings

Routing New Documents and Searches to the Appropriate Shards

From Elasticsearch in Action
Routing new documents and searches to the appropriate shards

Documents Going through Multiple Levels of Aggregations

From Elasticsearch in Action
Documents going through multiple levels of aggregations

Analysis Takes the Original Text and Makes Relevant Tokens Out of It

From Elasticsearch in Action
Analysis takes the original text and makes relevant tokens out of it

Simple Model of a Search Engine Based on Possible Interactions

From Relevant Search

Relevant Search Diagram

© 2020 Manning — Design Credits