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 descriptions. Moreover, feature salience is highly context-dependent, varying not only across items but also across users. Existing methods often rely on item titles, fixed attributes, or static item summaries, which limit personalized and fine-grained item understanding. To bridge this gap, we propose CAIRO, a user context-aware item profiling framework for LLM-based reranking...
| Authors | Dojun Hwang, Seunghan Lee, Cheonyoung Park et al. |
|---|---|
| Primary category | cs.IR |
| Published | 21 August 2026 |
| Last updated at source | 21 August 2026 |
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