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cs.IR2026
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…
cs.IR2026
FLAME: Condensing Ensemble Diversity into a Single Network for Efficient Sequential Recommendation
WooJoo Kim, JunYoung Kim, JaeHyung Lim +3
Sequential recommendation requires capturing diverse user behaviors, which a single network often fails to capture. While ensemble methods mitigate this, training multiple networks…
cs.IR2025
Continual Recommender Systems
Hyunsik Yoo, SeongKu Kang, Hanghang Tong
Modern recommender systems operate in uniquely dynamic settings: user interests, item pools, and popularity trends shift continuously, and models must adapt in real time without fo…