Main Blog

Explaining Learning to Rank Models with Tree Shap

Introduction A common problem with machine learning models is their interpretability and explainability.We create a dataset and we train a model to achieve a task, then we would like to understand how the model obtains those results. This is often quite difficult to understand, especially with very complex models. In this blog post, I would…

Apache Lucene Apache Solr Deep Learning ECIR European Conference Evaluation & User Behaviour Information Retrieval LambdaMART Learning To Rank Machine Learning Main Blog RankLib Recommender Systems Representation Search Topic Modeling

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…