The Importance of Online Testing in Learning to Rank – Part 1
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.
Anna Ruggero
April 29, 2020
DocValues VS Stored Fields: Apache Solr Features and Performance SmackDown
This blog post aims to explain Docvalues and Store fields in Apache Solr for operations in which they can be used interchangeably.
Elia Porciani
March 25, 2020
Introducing Weighted Synonyms in Apache Lucene/Solr
This blog post is about our latest contribution to the Apache Lucene project: introducing weighted synonyms to provide better query expansion.
Alessandro Benedetti
March 2, 2020
Music Information Retrieval: the Intervals Table
In this post we describe what is an Intervals Table and how to build it using a Behaviour-Driven-Development (BDD) approach.
Andrea Gazzarini
February 12, 2020
Apache Solr Atomic Updates: a Polymorphic Approach
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.
Andrea Gazzarini
January 28, 2020
Road to Rated Ranking Evaluator Enterprise
This blog is about the story of Rated Ranking Evaluator and the road to an addition to the framework: Rated Ranking Evaluator Enterprise!
Alessandro Benedetti
December 23, 2019
Entity Search with graph embeddings – Part 4 – Evaluation and conclusion
In this final part of the Entity Search with Graph Embeddings serie we see evaluation measures and results.
Anna Ruggero
December 17, 2019
Entity Search with Graph Embeddings – Part 3 – Documents and Retrieval
Third part of the journey into Entity Search trough embeddings. Focus of the post is the ranking phase.
Anna Ruggero
December 5, 2019
Entity Search with graph embeddings – Part 2 – Embeddings and clustering
In this blog post we continue our journey into Entity Search with graph embeddings. In part 2 we talk about embeddings and clustering.
Anna Ruggero
November 20, 2019
Entity Search with graph embeddings – Part 1 – Overview
Entity Search: how to build virtual documents leveraging on graph embeddings. How to exploit entity embeddings and clustering.
Anna Ruggero
November 14, 2019
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.
