SCoRD：用于基于大语言模型（LLM）推荐的语义辅助持续检索-重排序蒸馏
SCoRD: Semantic-Assisted Continual Retriever-Reranker Distillation for LLM-Based Recommendation
AI总结：
SCoRD是面向非平稳数据流的LLM推荐两阶段流程的持续知识蒸馏框架，通过语义推理助手实现检索器与重排序器的高效协同适配，在真实数据集上验证了其有效性。
AI中文摘要：
推荐系统越来越多地采用两阶段流程：基于ID的检索器检索候选项目，基于LLM的重排序器对其进行排名优化。为提升检索质量，通常采用重排序器到检索器的蒸馏方法，将重排序器的知识迁移给检索器。然而，实际部署中该流程需持续适配用户不断变化的兴趣与新产生的交互数据。直接反复更新LLM重排序器并蒸馏其最新知识的方案成本过高；仅更新检索器虽成本更低，但其有限的容量使其难以从稀疏数据中完成适配。本文提出SCoRD，这是一种针对非平稳数据流下基于LLM的重排序流程的持续知识蒸馏框架。SCoRD引入语义推理助手，将LLM推断用户潜在意图的能力蒸馏为可复用的意图级指导；它在低置信度序列上选择性地将重排序器知识蒸馏给检索器，指导仅针对检索器的更新而无需重复进行LLM推理，并将检索器生成的表示与意图漂移信号反馈给重排序器。在真实世界数据集上的实验表明，SCoRD可实现高效且有效的检索器-重排序器协同适配。
英文摘要：
Recommendation systems increasingly adopt a two-stage pipeline, where an ID-based retriever retrieves candidates and an LLM-based reranker refines their rankings. To improve retrieval quality, reranker-to-retriever distillation is commonly used to transfer the reranker's knowledge to the retriever. For practical deployment, however, this pipeline must continually adapt to evolving interests and incoming interactions. A naive solution is to repeatedly update the LLM reranker and distill its latest knowledge, but this incurs prohibitive costs. Updating the retriever alone is cheaper, but its limited capacity makes adaptation from sparse data difficult. We propose SCoRD, a continual knowledge distillation framework for LLM-based reranking pipelines under a non-stationary data stream. SCoRD introduces a semantic reasoning assistant that distills the LLM's ability to infer underlying user intents into reusable intent-level guidance. It selectively distills reranker knowledge to the retriever on low-confidence sequences, guides retriever-only updates without repeated LLM inference, and feeds retriever-derived representations and intent-drift signals back to the reranker. Experiments on real-world datasets show that SCoRD enables effective and efficient retriever-reranker co-adaptation.
