Vector quantization, renowned for its unparalleled feature compression capabilities, has been a prominent topic in signal processing and machine learning research for several decades and remains widely utilized today. With the emergence of large models and generative AI, vector quantization has gained popularity in recommender systems, establishing itself as a preferred solution. This paper starts with a comprehensive review of vector quantization techniques. It then explores systematic taxonomies of vector quantization methods for recommender systems (VQ4Rec), examining their applications from multiple perspectives. Further, it provides a thorough introduction to research efforts in diverse recommendation scenarios, including efficiency-oriented approaches and quality-oriented approaches. Finally, the survey analyzes the remaining challenges and anticipates future trends in VQ4Rec, including the challenges associated with the training of vector quantization, the opportunities presented by large language models, and emerging trends in multimodal recommender systems. We hope this survey can pave the way for future researchers in the recommendation community and accelerate their exploration in this promising field.

翻译：向量量化以其无与伦比的特征压缩能力而闻名，几十年来一直是信号处理和机器学习研究中的热门课题，至今仍被广泛应用。随着大模型和生成式人工智能的出现，向量量化在推荐系统中日益流行，成为首选解决方案。本文首先对向量量化技术进行了全面回顾，然后系统地梳理了推荐系统中向量量化方法（VQ4Rec）的分类体系，从多个视角探讨其应用。此外，本文深入介绍了在多种推荐场景中的研究工作，包括面向效率的方法和面向质量的方法。最后，这篇综述分析了VQ4Rec领域尚存的挑战并展望了未来趋势，包括与向量量化训练相关的挑战、大语言模型带来的机遇，以及多模态推荐系统中的新兴趋势。我们希望这篇综述能够为推荐社区的未来研究者铺平道路，加速他们在这一前景广阔领域的探索。