Factorization Machine

Factorization machines (FMs) are machine learning models designed to efficiently model feature interactions in sparse data, primarily used in recommendation systems and other applications requiring high-throughput predictions. Current research focuses on improving FM efficiency and accuracy through techniques like low-rank decompositions, novel regularization methods, and optimized binary encoding schemes, as well as integrating FMs into larger architectures such as stacked FMs and hybrid Bayesian networks. These advancements aim to enhance the scalability and predictive power of FMs, leading to improved performance in large-scale applications like online advertising and personalized recommendations.