“Learning-To-Rank” is a method used in machine learning to train models to rank items or list of items in a particular order based on their relevance or importance.


Imagine you have a box of different toys, and you want to line them up from your least favorite to your most favorite. “Learning-To-Rank” is like a machine learning model doing the same thing, but with things like search results or product recommendations.

In-depth explanation

“Learning to Rank” (LTR) refers to the use of machine learning algorithms for the task of ranking a sequence of items in a specific order of relevance or importance. Machine learning models in LTR are trained using ground-truth orderings of different items, and the goal is to apply that learned ordering on new, unseen data.

LTR is most commonly applied in the realm of information retrieval, where the goal is to retrieve a set of documents from a larger corpus that are most relevant to a user’s search query. An LTR algorithm would be tasked with determining the order or rank of the documents based on a query. These algorithms learn the ranking function from training data, which is usually hand-labelled by humans.

LTR is an intermediary step between classification and regression (predicting continuous values) and can be approached through pointwise, pairwise or listwise methods.

Pointwise methods treat each item independently and use traditional machine learning techniques to predict its score or relevance. The assumption here is that each item’s score is independent of the others.

Pairwise methods focus on the relative order of a pair of items. The goal is to minimize the number of instances where a lower-ranked item is scored higher than a higher-ranked item. Instead of directly predicting the relevance score, they predict the relative order or preference of pairs.

Listwise methods, on the other hand, aim to optimize the value of an evaluation measure over the whole set of items. They consider the entire item list in their loss functions.

Common algorithms used in LTR include Random Forests, Boosted Trees, Neural Networks, and Support Vector Machines, among others.

Relevance, Information Retrieval, Supervised Learning, Unsupervised Learning, Boosting, Ranking SVM, Neural Networks, Decision Trees