Categorical Features in Apache Solr Learning to Rank
In this blog post we present the available learning to rank Apache Solr features with a focus on categorical features and how to manage them.
Anna Ruggero
October 6, 2022
Train and Test Sets Split for Evaluating Learning To Rank Models – Part 2
What is the most appropriate approach to handle queries when splitting data when evaluating learning to rank models?
Ilaria Petreti
September 13, 2022
Drop constant features: a real-world Learning To Rank scenario
Does removing constant features affect model performance? Find out with our real-world Learning to Rank application
Ilaria Petreti
June 16, 2021
A Learning to Rank Project on a Daily Song Ranking Problem – Part 4
Query-level features and under-sampled queries, how to handle them? Find it out, with our new Learning to Rank implementations
Ilaria Petreti
May 31, 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
Apache Solr Learning to Rank Feature Stores and Models
How to list, upload, delete feature stores and models necessary in Solr for learning to rank.
Anna Ruggero
February 25, 2021
A Learning to Rank Project on a Daily Song Ranking Problem – Part 2
This blog post is about several analysis on a LTR model and its explanation using the open source library SHAP
Ilaria Petreti
February 15, 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
Apache Solr Learning To Rank Interleaving
Interleaving is an online evaluation approach for ranking functions, contributed to Apache Solr Learning to Rank by Sease.
Alessandro Benedetti
December 4, 2020
Explaining Learning to Rank Models with Tree Shap
Explainability and Interpretability of Learning To Rank models are vital in Information Retrieval, in this blog we present Tree SHAP.
Anna Ruggero
July 15, 2020
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.
