3 papers
cs.IR2026
Profiling What Matters: Context-Aware Item Profiles from Large-Scale Metadata for LLM Recommenders
Dojun Hwang, Seunghan Lee, Cheonyoung Park +2
While Large Language Models (LLMs) have significantly advanced reranking in recommendation, effectively leveraging item-side information remains challenging. Real-world items are d…
cs.CL2025
NMIXX: Domain-Adapted Neural Embeddings for Cross-Lingual eXploration of Finance
Hanwool Lee, Sara Yu, Yewon Hwang +4
General-purpose sentence embedding models often struggle to capture specialized financial semantics, especially in low-resource languages like Korean, due to domain-specific jargon…
cs.CL2025
TWICE: What Advantages Can Low-Resource Domain-Specific Embedding Model Bring? -- A Case Study on Korea Financial Texts
Yewon Hwang, Sungbum Jung, Hanwool Lee +1
Domain specificity of embedding models is critical for effective performance. However, existing benchmarks, such as FinMTEB, are primarily designed for high-resource languages, lea…