# Semantics Meet Signals: Dual Codebook Representationl Learning for Generative Recommendation

arXiv: https://arxiv.org/abs/2511.20673 · HTML: https://mlsanity.com/paper/2511.20673/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 26 references that are in the map.
- First posted on arXiv 15 Nov 2025.

## Builds on (most cited first, 20 of 20)

- [Self-Attentive Sequential Recommendation](https://mlsanity.com/paper/1808.09781/) (2018)
- [Neural Collaborative Filtering](https://mlsanity.com/paper/1708.05031/) (2017)
- [BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer](https://mlsanity.com/paper/1904.06690/) (2019)
- [Autoregressive Image Generation using Residual Quantization](https://mlsanity.com/paper/2203.01941/) (2022)
- [Image-based Recommendations on Styles and Substitutes](https://mlsanity.com/paper/1506.04757/) (2015)
- [Personalized Top-N Sequential Recommendation via Convolutional Sequence Embedding](https://mlsanity.com/paper/1809.07426/) (2018)
- [Recommendation as Language Processing (RLP): A Unified Pretrain, Personalized Prompt & Predict Paradigm (P5)](https://mlsanity.com/paper/2203.13366/) (2022)
- [S^3-Rec: Self-Supervised Learning for Sequential Recommendation with Mutual Information Maximization](https://mlsanity.com/paper/2008.07873/) (2020)
- [Adapting Large Language Models by Integrating Collaborative Semantics for Recommendation](https://mlsanity.com/paper/2311.09049/) (2023)
- [Text Is All You Need: Learning Language Representations for Sequential Recommendation](https://mlsanity.com/paper/2305.13731/) (2023)
- [Learning Vector-Quantized Item Representation for Transferable Sequential Recommenders](https://mlsanity.com/paper/2210.12316/) (2022)
- [Learnable Item Tokenization for Generative Recommendation](https://mlsanity.com/paper/2405.07314/) (2024)
- [Hierarchical Gating Networks for Sequential Recommendation](https://mlsanity.com/paper/1906.09217/) (2019)
- [KuaiRand: An Unbiased Sequential Recommendation Dataset with Randomly Exposed Videos](https://mlsanity.com/paper/2208.08696/) (2022)
- [A Survey on Popularity Bias in Recommender Systems](https://mlsanity.com/paper/2308.01118/) (2023)
- [A Model of Two Tales: Dual Transfer Learning Framework for Improved Long-tail Item Recommendation](https://mlsanity.com/paper/2010.15982/) (2020)
- [EdVAE: Mitigating Codebook Collapse with Evidential Discrete Variational Autoencoders](https://mlsanity.com/paper/2310.05718/) (2023)
- [HiD-VAE: Interpretable Generative Recommendation via Hierarchical and Disentangled Semantic IDs](https://mlsanity.com/paper/2508.04618/) (2025)
- [Unified Semantic and ID Representation Learning for Deep Recommenders](https://mlsanity.com/paper/2502.16474/) (2025)
- [Semantic Codebook Learning for Dynamic Recommendation Models](https://mlsanity.com/paper/2408.00123/) (2024)

## Built on by (most cited first, 1 of 1)

- [CRAB: Codebook Rebalancing for Bias Mitigation in Generative Recommendation](https://mlsanity.com/paper/2604.05113/) (2026)

## Abstract (arXiv)

Generative recommendation has recently emerged as a powerful paradigm that unifies retrieval and generation, representing items as discrete semantic tokens and enabling flexible sequence modeling with autoregressive models. Despite its success, existing approaches rely on a single, uniform codebook to encode all items, overlooking the inherent imbalance between popular items rich in collaborative signals and long-tail items that depend on semantic understanding. We argue that this uniform treatment limits representational efficiency and hinders generalization. To address this, we introduce FlexCode, a popularity-aware framework that adaptively allocates a fixed token budget between a collaborative filtering (CF) codebook and a semantic codebook. A lightweight MoE dynamically balances CF-specific precision and semantic generalization, while an alignment and smoothness objective maintains coherence across the popularity spectrum. We perform experiments on both public and industrial-scale datasets, showing that FlexCode consistently outperform strong baselines. FlexCode provides a new mechanism for token representation in generative recommenders, achieving stronger accuracy and tail robustness, and offering a new perspective on balancing memorization and generalization in token-based recommendation models.