This blog post explores the Apache Solr multi-field search limitations with a focus on the sow(split on whitespace) parameter.
This blog post aims to illustrate how to generate the query Id and how to manage the creation of the Training Set
This blog post is about several analysis on a LTR model and its explanation using the open source library SHAP
In this blog post, the elasticsearch _source field is compared with stored fields and docvalues from a performance point of view
The Rated Ranking Evaluator (RRE) is an offline search quality evaluation library for both Apache Solr and Elasticsearch.
This blog post aims to illustrate step by step a Learning to Rank project on a Daily Song Ranking problem using open source libraries.
Interleaving is an online evaluation approach for ranking functions, contributed to Apache Solr Learning to Rank by Sease.
In this post we describe what is an Intervals Table and how to build it using a Behaviour-Driven-Development (BDD) approach.
Explainability and Interpretability of Learning To Rank models are vital in Information Retrieval, in this blog we present Tree SHAP.
Secrets of Interleaving approaches for Learning To Rank online testing/evaluation. It includes implementation details and pro/cons analysis.
We are Sease, an Information Retrieval Company based in London, focused on providing R&D project guidance and implementation, Search consulting services, Training, and Search solutions using open source software like Apache Lucene/Solr, Elasticsearch, OpenSearch and Vespa.
