用分布对齐提升生成式推荐，解决代码级监督信息丢失问题。
SODA: Semantic-Oriented Distributional Alignment for Generative Recommendation
- 以多层码本概率分布为软标签，替代传统离散代码监督。
- 在多个真实数据集上显著提升不同生成推荐模型的性能。
- 适合作为通用模块嵌入各类生成推荐系统，易用性强。
生成式推荐通过在紧凑的令牌空间中操作，成为传统检索-排序流水线的可扩展替代方案。然而，现有方法主要依赖离散代码级别的监督，导致信息损失，并限制了分词器与生成推荐器之间的联合优化。本文提出一种分布级监督范式，利用多层码本上的概率分布作为丰富且柔性的表示。基于此，我们设计了语义导向的分布对齐（SODA），一个基于贝叶斯个性化排名的即插即用对比监督框架，通过负KL散度对齐语义丰富的分布，同时支持端到端可微训练。在多个真实世界数据集上的大量实验表明，SODA能持续提升多种生成推荐骨干模型的性能，验证了其有效性和通用性。代码将在录用后公开。
原文摘要 · Abstract (English)
Generative recommendation has emerged as a scalable alternative to traditional retrieve-and-rank pipelines by operating in a compact token space. However, existing methods mainly rely on discrete code-level supervision, which leads to information loss and limits the joint optimization between the tokenizer and the generative recommender. In this work, we propose a distribution-level supervision paradigm that leverages probability distributions over multi-layer codebooks as soft and information-rich representations. Building on this idea, we introduce Semantic-Oriented Distributional Alignment (SODA), a plug-and-play contrastive supervision framework based on Bayesian Personalized Ranking, which aligns semantically rich distributions via negative KL divergence while enabling end-to-end differentiable training. Extensive experiments on multiple real-world datasets demonstrate that SODA consistently improves the performance of various generative recommender backbones, validating its effectiveness and generality. Codes will be available upon acceptance.
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