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.

翻译：推荐系统是数字时代的重要工具，在电子商务、娱乐和社交媒体等领域为用户提供个性化内容。在构建这些系统的众多方法中，潜在因子模型已被证明尤为有效。本文对推荐系统中的潜在因子模型进行了系统性综述，重点关注其核心原理、方法论及最新进展。本文通过一个涵盖学习数据、模型架构、学习策略和优化技术的结构化框架来审视相关文献。分析内容包括贡献的分类法，以及对所用学习数据类型（如隐式反馈、信任数据和内容数据）、各种模型（如概率模型、非线性模型和神经网络模型）的详细讨论，并探讨了多种学习策略，如在线学习、迁移学习和主动学习。此外，本文还讨论了用于训练潜在因子模型的优化策略，这些策略提升了模型的性能和可扩展性。通过识别趋势、差距及潜在研究方向，本综述旨在为寻求推动推荐系统领域发展的研究人员和实践者提供有价值的见解。