SODA: Semantic-Oriented Distributional Alignment for Generative Recommendation

Ziqi Xue; Dingxian Wang; Yimeng Bai; Shuai Zhu; Jialei Li; Xiaoyan Zhao; Frank Yang; Andrew Rabinovich; Yang Zhang; Pablo N. Mendes

arXiv · 2603.00700

SODA: Semantic-Oriented Distributional Alignment for Generative Recommendation

Ziqi Xue·Dingxian Wang·Yimeng Bai·Shuai Zhu·Jialei Li·Xiaoyan Zhao·Frank Yang·Andrew Rabinovich·Yang Zhang·Pablo N. Mendes

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Abstract

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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Ziqi Xue, Dingxian Wang, Yimeng Bai, Shuai Zhu, Jialei Li, Xiaoyan Zhao, Frank Yang, Andrew Rabinovich, Yang Zhang, Pablo N. Mendes. 2026-02-28. SODA: Semantic-Oriented Distributional Alignment for Generative Recommendation. https://arxiv.org/abs/2603.00700

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