SODA: Semantic-Oriented Distributional Alignment for Generative Recommendation
Fuente:
arXiv
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| Autori principali: | , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866912934385418240 |
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| author | Xue, Ziqi Wang, Dingxian Bai, Yimeng Zhu, Shuai Li, Jialei Zhao, Xiaoyan Yang, Frank Rabinovich, Andrew Zhang, Yang Mendes, Pablo N. |
| author_facet | Xue, Ziqi Wang, Dingxian Bai, Yimeng Zhu, Shuai Li, Jialei Zhao, Xiaoyan Yang, Frank Rabinovich, Andrew Zhang, Yang Mendes, Pablo N. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_00700 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | SODA: Semantic-Oriented Distributional Alignment for Generative Recommendation Xue, Ziqi Wang, Dingxian Bai, Yimeng Zhu, Shuai Li, Jialei Zhao, Xiaoyan Yang, Frank Rabinovich, Andrew Zhang, Yang Mendes, Pablo N. Information Retrieval 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. |
| title | SODA: Semantic-Oriented Distributional Alignment for Generative Recommendation |
| topic | Information Retrieval |
| url | https://arxiv.org/abs/2603.00700 |