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，通过为用户提供个性化推荐提升服务体验。嵌入技术作为关键环节，将用户和物品ID等高维离散特征转化为低维连续向量，显著优化了推荐性能。通过应用嵌入技术捕获复杂实体关系，相关研究已取得丰硕成果。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、自监督学习和基于图的方法。协同过滤通过生成捕获用户-物品偏好的嵌入，在稀疏数据场景中表现优异；自监督方法利用对比或生成式学习应用于多样化任务；基于图的技术如node2vec则通过挖掘网络环境中的复杂关系实现突破。针对嵌入方法固有的可扩展性挑战，本文深入探讨了推荐系统领域的创新方向——包括自动化机器学习（AutoML）、哈希技术和量化技术——旨在提升性能并降低计算复杂度。我们系统评述了各类架构与技术，揭示了该领域面临的挑战与未来研究方向。本综述力求全面呈现这一快速演进领域的前沿进展，为推荐系统领域的研究者与实践者提供有价值的参考。