Learning to rank (LTR) is a method that is used in the construction of classification models for information retrieval systems. The training data consists of lists of articles with an induced partial order that gives a numerical or ordinal score, or a binary judgment for each article. The purpose of the model is to order the elements into new lists according to the scores that take into account the judgments obtained from the articles.
Implementing ranking algorithms
Getting ready
In this recipe, we will use the pyltr package, which is a Python LTR toolkit with ranking models, evaluation metrics, and data-wrangling helpers.
