Apache Solr Learning To Rank Feature Extraction and qTime
How a learning to rank query works in Solr? How we can obtain the required features extraction time from the Solr qTime parameter?
Anna Ruggero
January 12, 2022
From Training to Ranking: Using BERT to Improve Search Relevance
Learning about text ranking using Deep Learning with BERT transformer. From training to neural re-ranking, with code snippets and examples.
Antonio Mallia
December 22, 2021
Artificial Intelligence Applied to Search: Introduction
How does Artificial Intelligence impact Search? This post explores the state of the art of AI applied to Information Retrieval in Open Source.
Alessandro Benedetti
July 7, 2021
A Learning to Rank Project on a Daily Song Ranking Problem – Part 3
This blog post aims to illustrate how to generate the query Id and how to manage the creation of the Training Set
Ilaria Petreti
March 25, 2021
A Learning to Rank Project on a Daily Song Ranking Problem
This blog post aims to illustrate step by step a Learning to Rank project on a Daily Song Ranking problem using open source libraries.
Ilaria Petreti
December 18, 2020
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
ECIR 2018 Experience
This blog is a quick summary of our experience at the ECIR 2018, the European Conference on Information Retrieval.
Alessandro Benedetti
April 5, 2018
Solr Is Learning To Rank Better – Part 4 – Solr Integration
The focus here will be on the Apache Solr side configuration and usage with all the related tips and tricks.
Alessandro Benedetti
October 23, 2016
Solr Is Learning To Rank Better – Part 3 – Ltr tools
This blog post is about the Apache Solr Learning to Rank Tools : a set of utilities for the Solr LTR integration.
Alessandro Benedetti
August 30, 2016
Solr Is Learning To Rank Better – Part 2 – Model Training
This blog is about the model training for the Learning To Rank integration, after we modelled our dataset, collected the data and refined it.
Alessandro Benedetti
August 24, 2016
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.
