A Modèle de Facteur Latent is a statistical model that aims to explain données observées through hidden (latent) variables. It’s particularly popular in the context of systèmes de recommandation, where the goal is to predict user preferences based on past behaviors. These models assume that there are underlying factors that influence the observed interactions between users and items, such as movies, products, or services.
Dans une application typique, les utilisateurs et les éléments sont représentés dans un espace partagé espace latent, where each user and item is associated with a set of features that are not directly observable. For example, in a movie système de recommandation, the latent factors could include genres, themes, or even the age group of the viewers. By decomposing the user-item interaction matrix (e.g., ratings or purchase history) into these latent factors, the model can uncover relationships that are not immediately apparent from the raw data.
Les modèles de facteurs latents peuvent être implémentés en utilisant diverses techniques, avec Factorisation de matrice being one of the most common approaches. In this technique, the user-item interaction matrix is approximated by the product of two lower-dimensional matrices: one representing users and the other representing items. The model learns to optimize these matrices based on the observed interactions, allowing it to make predictions for unobserved interactions.
Dans l'ensemble, les modèles de facteur latent sont des outils puissants dans apprentissage automatique and data analysis, particularly in scenarios involving large datasets with many users and items, enabling personalized recommendations and improving user engagement.