# Vector Quantization for Recommender Systems: A Review and Outlook

arXiv: https://arxiv.org/abs/2405.03110 · HTML: https://mlsanity.com/paper/2405.03110/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 May 2024.

## Built on by (most cited first, 6 of 6)

- [CodeBrain: Bridging Decoupled Tokenizer and Multi-Scale Architecture for EEG Foundation Model](https://mlsanity.com/paper/2506.09110/) (2025)
- [Node Identifiers: Compact, Discrete Representations for Efficient Graph Learning](https://mlsanity.com/paper/2405.16435/) (2024)
- [Discrete Tokenization for Multimodal LLMs: A Comprehensive Survey](https://mlsanity.com/paper/2507.22920/) (2025)
- [A Simple Contrastive Framework Of Item Tokenization For Generative Recommendation](https://mlsanity.com/paper/2506.16683/) (2025)
- [HDTree: Generative Modeling of Cellular Hierarchies for Robust Lineage Inference](https://mlsanity.com/paper/2506.23287/) (2025)
- [Generalized Radius and Integrated Codebook Transforms for Differentiable Vector Quantization](https://mlsanity.com/paper/2602.01140/) (2026)

## Abstract (arXiv)

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.