3 papers
cs.IR2026
Every Preference Has Its Strength: Injecting Ordinal Semantics into LLM-Based Recommenders
Jiwon Jeong, Donghee Han, Sungrae Hong +2
Recent work has shown that large language models (LLMs) can enhance recommender systems by integrating collaborative filtering (CF) signals through hybrid prompting. However, most…
cs.IR2025
Rethinking LLM-Based Recommendations: A Personalized Query-Driven Parallel Integration
Donghee Han, Hwanjun Song, Mun Yong Yi
Recent studies have explored integrating large language models (LLMs) into recommendation systems but face several challenges, including training-induced bias and bottlenecks from…
cs.LG2025
Efficient Knowledge Tracing Leveraging Higher-Order Information in Integrated Graphs
Donghee Han, Daehee Kim, Minjun Lee +3
The rise of online learning has led to the development of various knowledge tracing (KT) methods. However, existing methods have overlooked the problem of increasing computational…