  Recommender systems are essential tools in the digital era, providing
personalized content to users in areas like e-commerce, entertainment, and
social media. Among the many approaches developed to create these systems,
latent factor models have proven particularly effective. This survey
systematically reviews latent factor models in recommender systems, focusing on
their core principles, methodologies, and recent advancements. The literature
is examined through a structured framework covering learning data, model
architecture, learning strategies, and optimization techniques. The analysis
includes a taxonomy of contributions and detailed discussions on the types of
learning data used, such as implicit feedback, trust, and content data, various
models such as probabilistic, nonlinear, and neural models, and an exploration
of diverse learning strategies like online learning, transfer learning, and
active learning. Furthermore, the survey addresses the optimization strategies
used to train latent factor models, improving their performance and
scalability. By identifying trends, gaps, and potential research directions,
this survey aims to provide valuable insights for researchers and practitioners
looking to advance the field of recommender systems.
