统一生成与排序，提升推荐系统精准度
Harmonizing Generative Retrieval and Ranking in Chain-of-Recommendation
- 用单一Transformer模型分步生成候选并实时排序
- 在真实数据集上实现更优的Top-K推荐效果
- 适合需要兼顾生成能力与排序精度的推荐场景
生成式推荐系统通过自回归生成语义ID来预测下一个物品，如OneRec系列工作。然而，由于缺乏对下一物品的感知，该方法虽能生成大量候选（如从beam-256中选出），却难以评估其优劣，导致生成与排序性能之间存在差距。为此，我们提出RecoChain，一个统一的生成检索与排序框架，将候选生成与排序整合于同一Transformer主干网络中。推理时，模型先通过分层语义ID预测生成候选物品，再基于SIM进行连续点击概率估计，实现高效排序。大规模真实数据集上的实验表明，该方法有效弥合了生成与排序间的差距，在保持强大生成能力的同时，显著提升了Top-K推荐性能。
原文摘要 · Abstract (English)
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.
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