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.

翻译：生成式推荐（GR）将推荐任务重构为下一令牌生成问题，并已在工业应用中展现出潜力。然而，将GR扩展至工业广告领域具有显著挑战，因为系统需同时优化用户兴趣与商业价值。现有GR流程仍以语义为中心，难以在分词、解码及在线服务环节对齐价值信号。针对此问题，我们提出UniVA——面向广告推荐的统一价值对齐框架。首先引入商业化SID分词器，将价值相关属性注入SID构建过程，生成价值判别性物品表征；随后开发联合监督学习与eCPM感知强化学习优化的"生成即排序"SID解码器，通过融合价值分数到下一物品SID生成过程，实现在单次解码中完成生成与排序；最后设计价值引导的个性化束搜索，复用"生成即排序"逻辑作为在线价值引导信号，采用个性化前缀树约束解码路径至请求有效的SID路径。在腾讯微信视频号广告平台的实验中，UniVA在离线Hit Rate@100指标上较基线提升37.04%，线上A/B测试中GMV提升1.5%。