London Information Retrieval Meetup June

After the very warm reception of the first year, the fifth London Information Retrieval Meetup is approaching (23/06/2020) and we are excited to add more details about our speakers and talks!The event is going to be fully remote (given the COVID-19 situation) and free! You are invited to register : https://www.meetup.com/London-Information-Retrieval-Meetup-Group/events/270905716/ Our second speaker is … Continue reading London Information Retrieval Meetup June

Online Testing for Learning To Rank: Interleaving

If you have read Part 1 of this blog post, you should know by now how many fantastic things can be done with online testing! In particular, the advantages that interleaving brings with respect to A/B testing, but you are still waiting for the answer to a question: how to implement it? Let's see together … Continue reading Online Testing for Learning To Rank: Interleaving

The Importance of Online Testing in Learning to Rank – Part 1

You have just trained a learning to rank model and you now want to know how it performs. You can start by looking at the evaluation parameters returned by the train on the test set, but you are still not sure of which will be the impact in using it in a real website. This … Continue reading The Importance of Online Testing in Learning to Rank – Part 1

ECIR 2018 Experience

This blog is a quick summary of my (subjective) experience at ECIR 2018 : the 40th European Conference on Information Retrieval, hosted in Grenoble (France) from 26/03/2018 to 29/03/2018. Deep Learning and Explicability Eight long papers accepted were about Deep Learning. The topics "Neural Network" and "Word Embedding" were the most occurring in the accepted … Continue reading ECIR 2018 Experience

Solr Is Learning To Rank Better – Part 4 – Solr Integration

Last Stage Of The Journey This blog post is about the Apache Solr Learning To Rank ( LTR ) integration. We modelled our dataset, we collected the data and refined it in Part 1 . Trained the model in Part 2 . Analysed and evaluate the model and training set in Part 3 . We … Continue reading Solr Is Learning To Rank Better – Part 4 – Solr Integration

Solr Is Learning To Rank Better – Part 3 – Ltr tools

Apache Solr Learning to Rank - Things Get Serious This blog post is about the Apache Solr Learning to Rank Tools : a set of tools to ease the utilisation of the Apache Solr Learning To Rank integration. The model has been trained in Part 2, we are ready to deploy it to Solr, but … Continue reading Solr Is Learning To Rank Better – Part 3 – Ltr tools

Solr Is Learning To Rank Better – Part 2 – Model Training

Model Training For Apache Solr Learning To Rank  If you want to train a model for Apache Solr Learning To Rank , you are in the right place. This blog post is about the model training phase for the Apache Solr Learning To Rank integration. We modelled our dataset, we collected the data and refined it … Continue reading Solr Is Learning To Rank Better – Part 2 – Model Training

Solr Is Learning To Rank Better – Part 1 – Data Collection

Learning To Rank In Apache Solr Introduction This blog post is about the journey necessary to bring Learning To Rank In Apache Solr search engines. Learning to Rank[1] is the application of Machine Learning in the construction of ranking models for Information Retrieval systems. Introducing supervised learning from user behaviour and signals can improve the relevancy … Continue reading Solr Is Learning To Rank Better – Part 1 – Data Collection

Solr Document Classification – Part 1 – Indexing Time

Introduction This blog post is about the Solr classification module and the way Lucene classification has been integrated at indexing time. In the previous blog [1] we have explored the world of Lucene Classification and the extension to use it for Document Classification . It comes natural to integrate Solr with the Classification module and … Continue reading Solr Document Classification – Part 1 – Indexing Time

Lucene Document Classification

Introduction This blog post describes the approach used in the Lucene Classification module to adapt text classification to document ( multi field ) classification. Machine Learning and Search have been always strictly associated. Machine Learning can help to improve the Search Experience in a lot of ways, extracting more information from the corpus of documents, … Continue reading Lucene Document Classification