6 papers
Personalized and Multi-View Representation for Federated Cold-Start Recommendation
Jaehyung Lim, Wonbin Kweon, Woojoo Kim +3
Federated recommendation (FedRec) enables personalized modeling without centralizing users' interaction histories, but most existing methods assume a fixed item pool and thus overl…
TRACER: Balancing Stability-Plasticity-Cognitivity Trilemma for LLM Enhanced Continual Recommendation
WooJoo Kim, HyunSik Yoo, JunYoung Kim +3
Continual recommendation aims to capture evolving user interests from streaming data but struggles with sparsity. LLM enhancers mitigate this with semantic knowledge, but naive int…
GOD: Enhancing Generalization via Deep Grafting for Sequential Recommendation
WooJoo Kim, JunYoung Kim, JaeHyung Lim +1
Sequential recommenders often struggle with sparse and noisy histories, limiting generalization to unseen interactions. Knowledge distillation mitigates this by transferring dense…
From Overlooked to Explored: Recovering Item Relations via Mixture of Perspectives for Sequential Recommendation
Junyoung Kim, Wonbin Kweon, Woojoo Kim +3
Capturing user preference from a user's interaction sequence is the central challenge of Sequential Recommendation (SR). This preference intuitively emerges from inter-item relatio…
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…
Federated Continual Recommendation
Jaehyung Lim, Wonbin Kweon, Woojoo Kim +4
The increasing emphasis on privacy in recommendation systems has led to the adoption of Federated Learning (FL) as a privacy-preserving solution, enabling collaborative training wi…