  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.
