Дослідження роботи алгоритму ML.NET для обробки користувацьких рекомендацій

Abstract

This work explores the ML.NET algorithm for processing user recommendations. It addresses the challenge of large-scale data and diverse user preferences. The research employs matrix factorization and collaborative filtering techniques. The MatrixFactorizationTrainer is central in learning latent factors for users and items. An Alternating Least Squares (ALS) method is used to optimize predictions. Key hyperparameters such as latent dimensionality, iteration count, and regularization are tuned. The study demonstrates ML.NET’s capacity to build scalable, effective recommendation systems.