👉 Hotel computing, also known as hotel algorithms or matrix factorization techniques, is a machine learning approach used to address the problem of latent factor modeling in recommendation systems, particularly for handling sparse user-item interaction data. It works by decomposing a large matrix representing user-item interactions into two lower-dimensional matrices: one representing the latent features of users and another representing the latent features of items. This decomposition captures the underlying patterns and relationships in the data, enabling the system to predict missing or sparse entries in the original matrix. By approximating the original user-item interaction matrix with a product of these two matrices, hotel computing allows for effective recommendations even when the data is incomplete or noisy. This technique is widely used in various applications, including movie recommendations, product suggestions, and personalized content delivery.
