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.

翻译：采用语义ID的生成式推荐系统（如TIGER (Rajput et al., 2023)）已成为序列推荐中广泛采用的竞争范式。然而，现有架构专为语义检索设计，并未解决通过广告收入实现商业变现及出价整合等商业检索问题。我们提出统一框架GEM-Rec，将商业相关性与变现目标直接融入生成序列。通过引入控制令牌解耦"是否展示广告"与"具体展示哪个商品"的决策，使模型能够直接从交互日志中学习有效投放模式（交互日志本身即反映过往成功的广告投放）。配合该方案，我们设计了出价感知解码机制以处理实时定价，将出价直接注入推理过程引导生成高价值商品。我们证明该方法可保障分配单调性，即更高出价弱单调提升广告展示概率且无需重训练模型。实验表明，GEM-Rec能使平台动态优化语义相关性与平台收益。