Query-level features and under-sampled queries, how to handle them? Find it out, with our new Learning to Rank implementations
This blog post explores the Apache Solr multi-field search limitations with a focus on the sow(split on whitespace) parameter.
This blog post aims to illustrate how to generate the query Id and how to manage the creation of the Training Set
This blog post is about several analysis on a LTR model and its explanation using the open source library SHAP
In this blogpost I want to explore what possibilities elasticsearch gives us for storing fields and retrieve them at query time from the performance point of view. In fact, lucene, the underlying library upon which elasticsearch and solr are built, provides two ways for storing and retrieving fields: stored fields and docvalues. In addition, elasticsearch…
Introduction With Rated Ranking Evaluator Enterprise approaching soon, we take the occasion of explaining in details why Offline Search Quality Evaluation is so important nowadays and what you can do already with the Rated Ranking Evaluator open-source libraries. More news will come soon as we are approaching the V1 release date. Stay tuned! Search Quality…
This blog post aims to illustrate step by step a Learning to Rank project on a Daily Song Ranking problem using open source libraries.
Interleaving is an online evaluation approach for ranking functions, contributed to Apache Solr Learning to Rank by Sease.
In this post we describe what is an Intervals Table and how to build it using a Behaviour-Driven-Development (BDD) approach.
Explainability and Interpretability of Learning To Rank models are vital in Information Retrieval, in this blog we present Tree SHAP.