L'apprentissage par classement (LTR) est une d'apprentissage automatique used primarily in la récupération d'informations systems, such as moteurs de recherche and systèmes de recommandation, to improve the ranking of items based on their relevance to a user query. The goal of LTR is to produce a ranking that better satisfies user intent compared to traditional ranking methods.
LTR utilise apprentissage supervisé, where models are trained on labeled data sets that indicate the relevance of items in relation to specific queries. These data sets can include various features, such as user clicks, purchase history, and item characteristics, which serve as input for the model. The output is a ranking score that helps determine the order in which items are presented to users.
Il existe plusieurs approches pour mettre en œuvre l'apprentissage du classement, généralement classées en trois types principaux :
- Pointwise : Treats each item individually and predicts a score for each item based on its fonctionnalités.
- Pairwise : Compares pairs of items and learns to predict which item should rank higher based on their features.
- Listwise : Considers the entire list of items at once and optimizes the ranking for the whole list rather than individual items or pairs.
LTR is widely used in various applications, including web search, e-commerce recommendations, and content discovery platforms, where providing the most relevant results is crucial for enhancing expérience utilisateur and engagement. By continuously learning from user interactions and feedback, LTR models can adapt over time, improving their ranking accuracy and effectiveness.