Papers by KyuHwan Yeom
Data-Efficient Adaptation to Contextual Shifts in LLM-based Conversational Recommendation (2026.findings-acl)
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| Challenge: | Existing data selection methods struggle to distinguish learnable samples under contextual shifts. |
| Approach: | They propose a framework agnostic to underlying large language model-based conversational recommender systems (CRSs) that captures user preferences through free-form conversations and generates contextually relevant recommendations. |
| Outcome: | The proposed framework outperforms baselines on three CRS benchmarks with real-world temporal splits. |