Author

Seunghan Lee

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Review Aug 2026

Profiling What Matters: Context-Aware Item Profiles from Large-Scale Metadata for LLM Recommenders

While Large Language Models (LLMs) have significantly advanced reranking in recommendation, effectively leveraging item-side information remains challenging. Real-world items are described by vast, heterogeneous, and unstructured metadata, where decision-relevant signals are often implicit, noisy, or buried in long des...

Dojun Hwang, Seunghan Lee, Cheonyoung Park et al. · 0 citations

Preprint Aug 2026

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

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 deploy...

S. Baek, Gyuseok Lee, Seunghan Lee et al. · 0 citations