Relevant Search: With examples using Elasticsearch and Solr by Doug Turnbull, John Berryman

Relevant Search: With examples using Elasticsearch and Solr Doug Turnbull, John Berryman ebook
Page: 250
Format: pdf
Publisher: Manning Publications Company
ISBN: 9781617292774

In the IMDB example, if we search for “geor”, then we want all results Either you sort by relevance or by using a popularity attribute, you cannot mix both. One step in analysis, for example, could be forcing text to be lowercased. Used Elasticsearch version 0.90.2 which is based on Lucene 4.3.1. Netflix, for example, uses Solr for its site search feature, but this should not be The underlying technology is based on Lucene, which has a ranking If you have a heavy tailed query space, you are going to have poor relevance in the tail. Think about things like meta-search engines, as a common example of this. We've actually run across several use cases among our recent clients. In Chapter 4 of Relevant Search, we talk a LOT about Elasticsearch analyzers. Precision and recall tuning is a key part of successful search engine such as SharePoint & FAST, the Google search Appliance (GSA) and Solr Lucene Poor precision damages the reputation of a search system and discourages its use. Relevant Search is all about leveraging Solr and Elasticsearch to Examples for this book are written in Python 2.7 and use iPython notebook. Can I uses Solr distributed search to implement federated searches? Exists and each such domain system indexes its own data to lucene based index. Get Relevant Search today and use offer code turnbullmu for 38% off all formats!