Papers by Hyeongjun Yang
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. |
LLMs as Knowledge Graph Refiners: Mitigating Factual Inconsistencies in Generative Knowledge Extraction (2026.acl-long)
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| Challenge: | Knowledge graphs (KGs) represent real-world entities and their relations in a structured form. |
| Approach: | They propose a framework that performs triple-level refinement on KGs constructed via GKE. |
| Outcome: | The proposed framework improves KG quality from diverse perspectives. |
CLICK: Contrastive Learning for Injecting Contextual Knowledge to Conversational Recommender System (2023.eacl-main)
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| Challenge: | Existing CRSs lack capturing comprehensive user preferences . existing systems lack contextual knowledge to capture user preferences from a dialogue context . |
| Approach: | They propose a Contrastive Learning approach for Injecting Contextual Knowledge from Reddit data to a CRS task. |
| Outcome: | The proposed approach captures a user preference from a dialogue context without items . it improves on the existing methods, and the results are published in the journal of cognitive science. |