Author

Seunghan Lee

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Review Aug 2026

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

CAIRO, a user context-aware item profiling framework for LLM-based reranking, first structures raw metadata and reviews into objective features and subjective traits, and employs a lightweight profiler to select the most relevant information for each user-item pair with limited serving-time overhead.

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

Preprint Aug 2026

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

Experiments on real-world datasets show that SCoRD enables effective and efficient retriever-reranker co-adaptation and introduces a semantic reasoning assistant that distills the LLM's ability to infer underlying user intents into reusable intent-level guidance.

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