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cs.IR2026
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…
cs.IR2025
G-UBS: Towards Robust Understanding of Implicit Feedback via Group-Aware User Behavior Simulation
Boyu Chen, Siran Chen, Zhengrong Yue +7
User feedback is critical for refining recommendation systems, yet explicit feedback (e.g., likes or dislikes) remains scarce in practice. As a more feasible alternative, inferring…
cs.IR2025
TransRec: Learning Transferable Recommendation from Mixture-of-Modality Feedback
Jie Wang, Fajie Yuan, Mingyue Cheng +6
Learning large-scale pre-trained models on broad-ranging data and then transfer to a wide range of target tasks has become the de facto paradigm in many machine learning (ML) commu…