SCoRD: Semantic-Assisted Continual Retriever-Reranker Distillation for LLM-Based Recommendation

2026-08-20

AI Summary

为解决推荐系统中重排器知识向检索器迁移的效率问题，SCoRD框架通过引入语义推理助手选择性地蒸馏知识，实现检索器与重排器的有效协同适应。

Abstract

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.

Problem

• Continual Adaptation
• Knowledge Distillation
• Retriever-Reranker Pipeline

Innovation

• Semantic-Assisted
• Continual Knowledge Distillation
• Non-stationary Data Stream
• Intent-Level Guidance
• Retriever-Reranker Co-adaptation