What is the most appropriate approach to handle queries when splitting data when evaluating learning to rank models?
In this post, we will describe how RREE discoveries and correlates the user query with the search engine queries.
Did you know you can generate explicit ratings using the plugin JCBP you can find inside Rated Ranking Evaluator Enterprise?
This blog is a tutorial to explore the results of a search quality evaluation of Elasticsearch and Solr search engines in RRE-Enterprise.
This blog is a tutorial on how to visualise the search quality evaluation results of Elasticsearch and Solr search engines in RRE-Enterprise.
How data splitting can be done and why it is important for the offline evaluation of Learning to Rank models?
This blog is a tutorial to run a search quality evaluation of Elasticsearch and Solr search engines in RRE-Enterprise.
This blog is a tutorial to define a data collection for search quality evaluation of Elasticsearch and Solr search engines in RRE-Enterprise.
We’ve seen in our previous blog post tutorial how to define the golden truth, a fundamental milestone for Search Quality Evaluation.This blog focuses on the set up of the target of the evaluation: your search engine based on top of Elasticsearch or Solr.RRE-Enterprise supports two modalities: Embedded: RRE-Enterprise spins up with Docker an Elasticsearch/Solr instance,…
The first step in any search quality evaluation process is to define the ground truth:a set of triplets <query, document, rating> that specifies how relevant is a document for a certain query.The ratings can be produced in an explicit or implicit approach: Explicit – a team of human judges(domain experts hopefully) assigns the rating for…