Semantics Meet Signals: Dual Codebook Representationl Learning for Generative Recommendation

• URL: http://arxiv.org/abs/2511.20673v1
• Date: Sat, 15 Nov 2025 05:51:38 GMT
• Title: Semantics Meet Signals: Dual Codebook Representationl Learning for Generative Recommendation
• Abstract summary: We introduce FlexCode, a popularity-aware framework that allocates a fixed token budget between a collaborative filtering codebook and a semantic codebook.<n>A lightweight MoE dynamically balances CF-specific precision and semantic generalization, while an alignment and objective smoothness maintains coherence across the popularity spectrum.<n>We perform experiments on both public and industrial-scale datasets, showing that FlexCode consistently outperforms strong baselines.
• Score: 39.88791326869038
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• CodeBind: Decoupled Representation Learning for Multimodal Alignment with Unified Compositional Codebook [24.224314755265556]
  We propose CodeBind, a framework that optimize multimodal representation spaces through a modality-shared-specific codebook design.<n>CodeBind decomposes features into shared components for semantic consistency and specific components for modality-unique details.<n>This design utilizes a compositional vector quantization scheme, where a shared codebook bridges modality gaps and modality-specific codebooks mitigate representation bias.
  arXiv  Detail & Related papers  (2026-05-18T11:56:19Z)
• PIT: A Dynamic Personalized Item Tokenizer for End-to-End Generative Recommendation [10.959841655014387]
  PIT is a dynamic Personalized Item Tokenizer framework for end-to-end generative recommendation.<n>It employs a co-generative architecture that harmonizes collaborative patterns through collaborative signal alignment.<n>Experiments on real-world datasets demonstrate that PIT consistently outperforms competitive baselines.
  arXiv  Detail & Related papers  (2026-02-09T11:28:56Z)
• FMBench: Adaptive Large Language Model Output Formatting [49.52930069696333]
  We present FMBench, a benchmark for adaptive Markdown output formatting.<n>Experiments on two model families show that SFT consistently improves semantic alignment.<n>Results also reveal an inherent trade-off between semantic and structural objectives.
  arXiv  Detail & Related papers  (2026-02-06T04:42:06Z)
• Token-Weighted Multi-Target Learning for Generative Recommenders with Curriculum Learning [8.152671524863083]
  We propose two complementary information-gain-based token-weighting strategies tailored to generative recommendation with semantic IDs.<n>We introduce a multi-target learning framework with curriculum learning that jointly optimize the two token-weighted objectives alongside standard likelihood.
  arXiv  Detail & Related papers  (2026-01-25T11:01:14Z)
• CoFiRec: Coarse-to-Fine Tokenization for Generative Recommendation [55.783414010717074]
  CoFiRec is a novel generative recommendation framework that decomposes item information into semantic levels.<n>We show that CoFiRec outperforms existing methods, offering a new perspective for generative recommendation.
  arXiv  Detail & Related papers  (2025-11-27T18:59:35Z)
• IAR2: Improving Autoregressive Visual Generation with Semantic-Detail Associated Token Prediction [77.06211178777939]
  IAR2 is an advanced autoregressive framework that enables a hierarchical semantic-detail synthesis process.<n>We show that IAR2 sets a new state-of-the-art for autoregressive image generation, achieving a FID of 1.50 on ImageNet.
  arXiv  Detail & Related papers  (2025-10-08T12:08:21Z)
• DiscRec: Disentangled Semantic-Collaborative Modeling for Generative Recommendation [33.152693125551785]
  Generative recommendation is emerging as a powerful paradigm that directly generates item predictions.<n>Current methods face two key challenges: token-item misalignment and semantic-collaborative signal entanglement.<n>We propose DiscRec, a novel framework that enables Disentangled Semantic-Collaborative signal modeling.
  arXiv  Detail & Related papers  (2025-06-18T15:53:47Z)
• Universal Item Tokenization for Transferable Generative Recommendation [89.42584009980676]
  We propose UTGRec, a universal item tokenization approach for transferable Generative Recommendation.<n>By devising tree-structured codebooks, we discretize content representations into corresponding codes for item tokenization.<n>For raw content reconstruction, we employ dual lightweight decoders to reconstruct item text and images from discrete representations.<n>For collaborative knowledge integration, we assume that co-occurring items are similar and integrate collaborative signals through co-occurrence alignment and reconstruction.
  arXiv  Detail & Related papers  (2025-04-06T08:07:49Z)
• Boosting Chart-to-Code Generation in MLLM via Dual Preference-Guided Refinement [16.22363384653305]
  Multimodal Large Language Models (MLLMs) perform fine-grained visual parsing, precise code synthesis, and robust cross-modal reasoning.<n>We propose a dual preference-guided refinement framework that combines a feedback-driven, dual-modality reward mechanism with iterative preference learning.<n>Our framework significantly enhances the performance of general-purpose open-source MLLMs, enabling them to generate high-quality plotting code.
  arXiv  Detail & Related papers  (2025-04-03T07:51:20Z)
• Harmonizing Visual Representations for Unified Multimodal Understanding and Generation [53.01486796503091]
  We present emphHarmon, a unified autoregressive framework that harmonizes understanding and generation tasks with a shared MAR encoder.<n>Harmon achieves state-of-the-art image generation results on the GenEval, MJHQ30K and WISE benchmarks.
  arXiv  Detail & Related papers  (2025-03-27T20:50:38Z)
• Universalizing Weak Supervision [18.832796698152492]
  We propose a universal technique that enables weak supervision over any label type. We apply this technique to important problems previously not tackled by WS frameworks including learning to rank, regression, and learning in hyperbolic space.
  arXiv  Detail & Related papers  (2021-12-07T17:59:10Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.