Generative recommendation has recently attracted widespread attention in industry due to its potential for scaling and stronger model capacity. However, deploying real-time generative recommendation in large-scale advertising requires designs beyond large-language-model (LLM)-style training and serving recipes. We present a production-oriented generative recommender co-designed across architecture, learning, and serving, named GR4AD (Generative Recommendation for ADdvertising). As for tokenization, GR4AD proposes UA-SID (Unified Advertisement Semantic ID) to capture complicated business information. Furthermore, GR4AD introduces LazyAR, a lazy autoregressive decoder that relaxes layer-wise dependencies for short, multi-candidate generation, preserving effectiveness while reducing inference cost, which facilitates scaling under fixed serving budgets. To align optimization with business value, GR4AD employs VSL (Value-Aware Supervised Learning) and proposes RSPO (Ranking-Guided Softmax Preference Optimization), a ranking-aware, list-wise reinforcement learning algorithm that optimizes value-based rewards under list-level metrics for continual online updates. For online inference, we further propose dynamic beam serving, which adapts beam width across generation levels and online load to control compute. Large-scale online A/B tests show up to 4.2% ad revenue improvement over an existing DLRM-based stack, with consistent gains from both model scaling and inference-time scaling. GR4AD has been fully deployed in Kuaishou advertising system with over 400 million users and achieves high-throughput real-time serving.

翻译：生成式推荐因其扩展潜力与更强的模型能力，近期在工业界引起了广泛关注。然而，在大规模广告系统中部署实时生成式推荐，需要超越大语言模型（LLM）风格的训练与服务方案的设计。我们提出了一种面向生产的生成式推荐系统，其在架构、学习与服务层面进行了协同设计，命名为GR4AD（面向广告的生成式推荐）。在标记化方面，GR4AD提出了UA-SID（统一广告语义标识符）以捕捉复杂的业务信息。此外，GR4AD引入了LazyAR，一种惰性自回归解码器，它放宽了层间依赖以进行短序列、多候选的生成，在保持有效性的同时降低了推理成本，从而有助于在固定的服务预算下实现扩展。为了使优化与业务价值对齐，GR4AD采用了VSL（价值感知监督学习），并提出了RSPO（排序引导的Softmax偏好优化），这是一种排序感知的、列表式强化学习算法，可在列表级指标下优化基于价值的奖励，用于持续的在线更新。对于在线推理，我们进一步提出了动态束搜索服务，它能根据生成层级和在线负载自适应调整束宽以控制计算量。大规模在线A/B测试显示，相较于现有的基于DLRM的推荐栈，广告收入提升高达4.2%，模型扩展和推理时扩展均带来了一致的收益提升。GR4AD已在拥有超过4亿用户的快手广告系统中全面部署，并实现了高吞吐量的实时服务。