Generative Recommendation (GR) reformulates recommendation as a next-token generation problem and has shown promise in industrial applications. However, extending GR to industrial advertising is non-trivial because the system must optimize not only user interest but also commercial value. Existing GR pipelines remain largely semantics-centric, making it difficult to align value signals across tokenization, decoding, and online serving. To address this issue, we propose UniVA, a Unified Value Alignment framework for advertising recommendation. We first introduce a Commercial SID tokenizer that injects value-related attributes into SID construction, yielding value-discriminative item representations. We then develop a Generation-as-Ranking SID Decoder jointly optimized by supervised learning and eCPM-aware reinforcement learning, which fuses value scores into next-item SID generation to perform generation and ranking in one decoding process. Finally, we design a value-guided personalized beam search that reuses generation-as-ranking logits as online value guidance and applies a personalized trie tree to constrain decoding to request-valid SID paths. Experiments on the Tencent WeChat Channels advertising platform show that UniVA achieves a 37.04\% improvement in offline Hit Rate@100 over the baseline and a 1.5\% GMV lift in online A/B tests.
@article{arxiv.2605.05803,
title = {Unified Value Alignment for Generative Recommendation in Industrial Advertising},
author = {Xinxun Zhang and Yuling Xiong and Jiale Zhou and Zhengkai Guo and Zhennan Pang and Junbang Huo and Jingwen Wang and Xuyang Sun and Enming Zhang and Jiaguang Jin and Changping Wang and Yi Li and Jun Zhang and Xiao Yan and Jiawei Jiang and Jie Jiang},
journal= {arXiv preprint arXiv:2605.05803},
year = {2026}
}