Generative recommender systems have recently emerged as a promising paradigm by formulating next-item prediction as an auto-regressive semantic IDs generation, such as OneRec series works. However, with the next-item-agnostic prediction paradigm, its could beam out some next potential items via Semantic IDs but hard to estimate which items are better from them, e.g., select the top-10 from beam-256 items, leading to a gap between generation and ranking performance. To fulfill this gap, we propose RecoChain, a unified generative retrieval and ranking framework that integrates candidate generation and ranking within a single Transformer backbone. Specifically, in inference, the model first generates candidate items via hierarchical semantic ID prediction, then performs the SIM-based ranking process to estimate the click possibility of corresponding item candidate continuously. Extensive experiments on large-scale real-world datasets demonstrate that our approach effectively bridges the gap between generative retrieval and ranking, achieving improved Top-K recommendation performance while maintaining strong generative capability.

翻译：生成式推荐系统近期作为一种新兴范式崭露头角，通过将下一项预测建模为自回归语义ID生成（如OneRec系列工作）。然而，由于采用与候选项目无关的预测范式，该类方法虽能通过语义ID生成若干潜在候选项，却难以评估这些候选项的优劣程度——例如从256个束搜索候选中选出前10项时，会导致生成与排序性能之间存在差距。为解决这一矛盾，我们提出RecoChain框架，这是一个将候选生成与排序整合到单一Transformer主干网络中的统一生成式检索与排序架构。具体而言，在推理阶段，模型首先通过分层语义ID预测生成候选项目，随后执行基于SIM的排序过程来连续评估对应候选项目的点击可能性。在大规模真实世界数据集上的实验表明，该方法有效弥合了生成式检索与排序之间的差距，在保持强大生成能力的同时，实现了更优的Top-K推荐性能。