User Behavior Modeling (UBM) plays a critical role in user interest learning, which has been extensively used in recommender systems. Crucial interactive patterns between users and items have been exploited, which brings compelling improvements in many recommendation tasks. In this paper, we attempt to provide a thorough survey of this research topic. We start by reviewing the research background of UBM. Then, we provide a systematic taxonomy of existing UBM research works, which can be categorized into four different directions including Conventional UBM, Long-Sequence UBM, Multi-Type UBM, and UBM with Side Information. Within each direction, representative models and their strengths and weaknesses are comprehensively discussed. Besides, we elaborate on the industrial practices of UBM methods with the hope of providing insights into the application value of existing UBM solutions. Finally, we summarize the survey and discuss the future prospects of this field.

翻译：用户行为建模（UBM）在用户兴趣学习中扮演着关键角色，已被广泛应用于推荐系统。研究者发掘了用户与物品之间的关键交互模式，这为诸多推荐任务带来了显著性能提升。本文力求对这一研究主题进行全面综述。我们首先回顾UBM的研究背景，随后提出现有UBM研究工作的系统性分类体系，将其归纳为四个不同方向：传统UBM、长序列UBM、多类型UBM以及含辅助信息的UBM。针对每个方向，我们深入探讨了代表性模型及其优劣之处。此外，我们详细阐述了UBM方法的工业实践，旨在揭示现有UBM解决方案的应用价值。最后，我们对综述进行总结，并展望该领域的未来发展方向。