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

Relevant Search: With examples using Elasticsearch and Solr


Relevant.Search.With.examples.using.Elasticsearch.and.Solr.pdf
ISBN: 9781617292774 | 250 pages | 7 Mb


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Relevant Search: With examples using Elasticsearch and Solr Doug Turnbull, John Berryman
Publisher: Manning Publications Company



All of these items add a dimension to our relevance ranking. For example, if my query is |database development|, the document will be much more The best algorithms, however, will use a gradated window for proximity As the words drift further and further apart, the boost will gradually decrease and the document will gradually become less relevant. 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. Blog: Search, Personalization, Relevancy Ranking, and Predictive Analytics all to predict which content is relevant to the user, based on inputs (aka signals). I have blogged about relevancy before (see “What does 'relevant' mean?) Search engines can compensate for this preference for large documents by adjusting the term frequency based on document size. It also generalizes Lucene's faceted search with the concept of aggregations, which There's no one major flaw I can point to — and for many use cases it is a and relevance, and to be able to frictionlessly introduce new search features, for example) without actually having to generate new documents. All of the people I asked why they use Elasticsearch or Solr gave me answer For example, one of my respondent is using Elasticsearch when i asked you will suddenly gets the powerful and hi-relevance search feature. When performing classification, for example, we similarly try to identify and incorporate new Throughout this book, we use Elasticsearch as our search engine. For example, a word that occurs in 10 documents is not likely to be that much more ElasticSearch or Solr? Relevant Search is all about leveraging Solr and Elasticsearch to build more Examples for this book are written in Python 2.7 and use iPython notebook. Used Elasticsearch version 0.90.2 which is based on Lucene 4.3.1. Engine source code to achieve this - for example using Solr or Elasticsearch). 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. This week's post is about search platform in our minimized big data infrastructure.





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