ElasticSearch tells us how relevant a document is through the score, to the extent that, for search results, the documents with the highest score are located at the top of the response json.
This is possible through an algorithm called BM25, the algorithm used today in Elasticsearch to calculate the score. This algorithm, which uses terms such as term frequency, inverse document frequency and field length, allows us to detect the relevance of a document by combining the concepts of precision and recall, all for the calculation of the score.
That's what we talk about today, what is the score, how it is calculated, and of course a practical example of score in Elastic search.
More details about the BM25 algorithm and score calculation: www.elastic.co...
More about precision and completeness (recall): dev.to/elastic...
The content:
00:03 - What is relevance in Elasticsearch
00:55 - Recall and precision in document search
03:14 - The BM25 algorithm
03:31 - Term frequency, inverse document frequency and field length
04:34 - Elastic search score example
07:05 - Filter patch for filtering ElasticSearch results
09:32 - Explain for score explanation
Watch all the videos of the elastic search course: • Elastic Search PASO a ...
Facebook: goo.gl/HDLFyn
Twitter: goo.gl/pcFziM
#elasticsearch #elasticsearchtutorial