5 citations · 8 across the 16 of their papers we have counts for
9 papers · 1 filter
Filling the Gaps: Selective Knowledge Augmentation for LLM Recommenders
Jaehyun Lee, Sanghwan Jang, SeongKu Kang +1
Large language models (LLMs) have recently emerged as powerful training-free recommenders. However, their knowledge of individual items is inevitably uneven due to imbalanced infor…
Personalized Federated Recommendation With Knowledge Guidance
Jaehyung Lim, Wonbin Kweon, Woojoo Kim +3
Federated Recommendation (FedRec) has emerged as a key paradigm for building privacy-preserving recommender systems. However, existing FedRec models face a critical dilemma: memory…
PairSem: LLM-Guided Pairwise Semantic Matching for Scientific Document Retrieval
Wonbin Kweon, Runchu Tian, SeongKu Kang +4
Scientific document retrieval is a critical task for enabling knowledge discovery and supporting research across diverse domains. However, existing dense retrieval methods often st…
Capturing User Interests from Data Streams for Continual Sequential Recommendation
Gyuseok Lee, Hyunsik Yoo, Junyoung Hwang +2
Transformer-based sequential recommendation (SR) models excel at modeling long-range dependencies in user behavior via self-attention. However, updating them with continuously arri…
Improving Scientific Document Retrieval with Concept Coverage-based Query Set Generation
SeongKu Kang, Bowen Jin, Wonbin Kweon +4
In specialized fields like the scientific domain, constructing large-scale human-annotated datasets poses a significant challenge due to the need for domain expertise. Recent metho…
Uncertainty Quantification and Decomposition for LLM-based Recommendation
Wonbin Kweon, Sanghwan Jang, SeongKu Kang +1
Despite the widespread adoption of large language models (LLMs) for recommendation, we demonstrate that LLMs often exhibit uncertainty in their recommendations. To ensure the trust…