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.
It is fundamental to online test your Learning To Rank system, this blog shows you how it can be implemented and the most common mistakes.
This blog post aims to give a better understanding of Docvalues and stored fields in Apache Solr for the operations in which they can be used interchangeably.
This blog post is about our latest contribution to the Apache Lucene project: introducing weighted synonyms to provide better query expansion.
In this post we describe what is an Intervals Table and how to build it using a Behaviour-Driven-Development (BDD) approach.
In this post we describe an approach to solve the problem of an application that requires both Full and Atomic Updates, using one of the powerful concepts in Object Oriented Programming: Polymorphism.
It was the spring of 2018, Andrea was strenuously working on a customer project, continuously tuning search configurations and checking the ground truth for certain queries manually. That was pretty much the standard at the time, the brilliant Quepid[1] from our friends at Open Source Connection helped in some use cases, but there was nothing…
In this final part of the Entity Search with Graph Embeddings serie we see evaluation measures and results.
Third part of the journey into Entity Search trough embeddings. Focus of the post is the ranking phase.