Matrix Factorization

Matrix factorization is a highly effective technique for recommendation systems, especially when large user-item interaction datasets are available. Its goal is to represent users and items in a low-dimensional latent factor space and learn preference structure from those embeddings. This approach has delivered strong results in both competitions and production systems for many years. However, cold-start issues for new users and new items remain a significant challenge.