3 papers
cs.IR2026
Why Thinking Hurts: Diagnosing and Rectifying Linguistic Inertia in Large Language Models for Recommendation
Luankang Zhang, Yonghao Huang, Hang Lv +6
Chain-of-Thought (CoT) reasoning is widely used to improve LLM performance, and recent foundation recommender models adopt it by generating textual reasoning before predicting targ…
cs.IR2026
Can Recommender Systems Teach Themselves? A Recursive Self-Improving Framework with Fidelity Control
Luankang Zhang, Hao Wang, Zhongzhou Liu +8
The scarcity of high-quality training data presents a fundamental bottleneck to scaling machine learning models. This challenge is particularly acute in recommendation systems, whe…
cs.IR2026
The Next Paradigm Is User-Centric Agent, Not Platform-Centric Service
Luankang Zhang, Hang Lv, Qiushi Pan +8
Modern digital services have evolved into indispensable tools, driving the present large-scale information systems. Yet, the prevailing platform-centric model, where services are o…