Recommender systems have become an essential component of many online platforms, providing personalized recommendations to users. A crucial aspect is embedding techniques that coverts the high-dimensional discrete features, such as user and item IDs, into low-dimensional continuous vectors and can enhance the recommendation performance. Applying embedding techniques captures complex entity relationships and has spurred substantial research. In this survey, we provide an overview of the recent literature on embedding techniques in recommender systems. This survey covers embedding methods like collaborative filtering, self-supervised learning, and graph-based techniques. Collaborative filtering generates embeddings capturing user-item preferences, excelling in sparse data. Self-supervised methods leverage contrastive or generative learning for various tasks. Graph-based techniques like node2vec exploit complex relationships in network-rich environments. Addressing the scalability challenges inherent to embedding methods, our survey delves into innovative directions within the field of recommendation systems. These directions aim to enhance performance and reduce computational complexity, paving the way for improved recommender systems. Among these innovative approaches, we will introduce Auto Machine Learning (AutoML), hash techniques, and quantization techniques in this survey. We discuss various architectures and techniques and highlight the challenges and future directions in these aspects. This survey aims to provide a comprehensive overview of the state-of-the-art in this rapidly evolving field and serve as a useful resource for researchers and practitioners working in the area of recommender systems.

翻译：推荐系统已成为众多在线平台的核心组件，致力于为用户提供个性化推荐。其中嵌入技术作为关键环节，能够将用户ID、物品ID等高维离散特征转化为低维连续向量，从而有效提升推荐性能。通过应用嵌入技术捕获复杂实体关系已引发大量研究。本综述系统梳理了推荐系统中嵌入技术的最新文献，涵盖协同过滤、自监督学习及基于图的技术等嵌入方法。协同过滤生成的嵌入可捕捉用户-物品偏好，在稀疏数据场景表现优异；自监督方法利用对比学习或生成学习处理多样化任务；基于图的技术（如node2vec）则能挖掘网络密集环境中的复杂关系。针对嵌入方法固有的可扩展性挑战，本综述深入探讨推荐系统领域的创新方向，这些方向旨在提升性能并降低计算复杂度，为改进推荐系统开辟路径。在创新方法中，我们将重点介绍自动机器学习（AutoML）、哈希技术与量化技术。本文讨论各类架构与技术，并着重分析这些领域的挑战与未来方向。本综述旨在为该快速发展领域提供前沿进展的全面概述，成为推荐系统研究者和实践者的重要参考资源。