让推荐模型同时考虑内容相关性和广告收益，自动优化广告投放。
One Model, Two Markets: Bid-Aware Generative Recommendation
- 用控制标记分离是否展示广告与展示哪个广告，从历史数据学投放策略。
- 引入实时出价解码机制，高报价广告更可能被推荐，且无需重新训练模型。
- 适合需要兼顾内容推荐和广告变现的平台使用，尤其在动态竞价场景中。
基于语义标识的生成式推荐系统（如 TIGER）已成为序列推荐的主流范式。然而现有架构仅关注语义检索，未考虑通过广告收入实现变现及结合出价进行商业检索的问题。本文提出 GEM-Rec，一个将商业相关性与变现目标统一集成到生成序列中的框架。通过引入控制标记，解耦是否展示广告与选择具体广告的决策，使模型能直接从用户交互日志中学习过往成功的广告投放模式。同时，设计了出价感知解码机制，在推理过程中注入实时出价信息，引导生成高价值广告。理论证明该方法保证分配单调性，即更高出价会弱增强广告被展示的概率，且无需模型重训练。实验表明，GEM-Rec 能使平台动态优化语义相关性与平台收益。
原文摘要 · Abstract (English)
Generative Recommender Systems using semantic ids, such as TIGER (Rajput et al., 2023), have emerged as a widely adopted competitive paradigm in sequential recommendation. However, existing architectures are designed solely for semantic retrieval and do not address concerns such as monetization via ad revenue and incorporation of bids for commercial retrieval. We propose GEM-Rec, a unified framework that integrates commercial relevance and monetization objectives directly into the generative sequence. We introduce control tokens to decouple the decision of whether to show an ad from which item to show. This allows the model to learn valid placement patterns directly from interaction logs, which inherently reflect past successful ad placements. Complementing this, we devise a Bid-Aware Decoding mechanism that handles real-time pricing, injecting bids directly into the inference process to steer the generation toward high-value items. We prove that this approach guarantees allocation monotonicity, ensuring that higher bids weakly increase an ad's likelihood of being shown without requiring model retraining. Experiments demonstrate that GEM-Rec allows platforms to dynamically optimize for semantic relevance and platform revenue.
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