6 citations · 6 across the 4 of their papers we have counts for
4 papers · 1 filter
SAGER: Self-Evolving User Policy Skills for Recommendation Agent
Zhen Tao, Riwei Lai, Chenyun Yu +7
Large language model (LLM) based recommendation agents personalize what they know through evolving per-user semantic memory, yet how they reason remains a universal, static system…
The Double-Edged Sword of Knowledge Transfer: Diagnosing and Curing Fairness Pathologies in Cross-Domain Recommendation
Yuhan Zhao, Weixin Chen, Li Chen +1
Cross-domain recommendation (CDR) offers an effective strategy for improving recommendation quality in a target domain by leveraging auxiliary signals from source domains. Nonethel…
MemRec: Collaborative Memory-Augmented Agentic Recommender System
Weixin Chen, Yuhan Zhao, Jingyuan Huang +6
The evolution of recommender systems has shifted from traditional collaborative filtering to LLM-based agentic systems, which rely on semantic user and item memories to make predic…
Leave No One Behind: Fairness-Aware Cross-Domain Recommender Systems for Non-Overlapping Users
Weixin Chen, Yuhan Zhao, Li Chen +1
Cross-domain recommendation (CDR) methods predominantly leverage overlapping users to transfer knowledge from a source domain to a target domain. However, through empirical studies…