most citedBalancing User Preferences by Social Networks: A Condition-Guided Social Recommendation Model for Mitigating Popularity Bias

8 citations

6 papers

cs.CL2026

TRACE: Discovering Task-Specific Parameter via Adaptation-Aware Probing for Continual Fine-Tuning

Xiaosong Han, Ke Chen, Xindi Dai +7

In real-world deployment, LLMs are often adapted continually across tasks to keep LLMs up-to-date in production, where new fine-tuning should preserve previously learned skills. Ho…

cs.AI2026

Advancing Graph Few-Shot Learning via In-Context Learning

Renchu Guan, Yajun Wang, Chunli Guo +5

Graph few-shot learning, which aims to classify nodes from novel classes with only a few labeled examples, is a widely studied problem in graph learning. However, existing methods…

cs.IR2026

Automatic Self-supervised Learning for Social Recommendations

Xin He, Wenqi Fan, Mingchen Sun +2

In recent years, researchers have leveraged social relations to enhance recommendation performance. However, most existing social recommendation methods require carefully designed…

cs.SI20268 cited

Balancing User Preferences by Social Networks: A Condition-Guided Social Recommendation Model for Mitigating Popularity Bias

Xin He, Wenqi Fan, Ruobing Wang +4

Social recommendation models weave social interactions into their design to provide uniquely personalized recommendation results for users. However, social networks not only amplif…

cs.CR20262 cited

Leveraging the Power of Ensemble Learning for Secure Low Altitude Economy

Yaoqi Yang, Yong Chen, Jiacheng Wang +3

Low Altitude Economy (LAE) holds immense promise for enhancing societal well-being and driving economic growth. However, this burgeoning field is vulnerable to security threats, pa…

eess.SP20262 cited

SIM-assisted Secure Mobile Communications via Enhanced Proximal Policy Optimization Algorithm

Wenxuan Ma, Bin Lin, Hongyang Pan +4

With the development of sixth-generation (6G) wireless communication networks, the security challenges are becoming increasingly prominent, especially for mobile users (MUs). As a…