Guiyuan Yuan — accepted AI-conference papers

2025

Dynamic Graph Multi-granularity Attribute Scene Evolution Sequence Recommendation

Longtao Wang, Qingtian Zeng, Guiyuan Yuan, Hua Duan, Cheng Cheng, Kai Jiang

ICASSP 2025accepted

The recommendation based on dynamic graph sequences aims to reveal complex evolutionary patterns in user-item interactions. Existing methods make predictions by encoding attribute contents through similarity but lack dynamic modeling of fine-grained attribute scenarios, resulting in a deviation in u…

Cited by 0 Source ↗Scholar ↗

2025

Heterogeneous Graph Dual-structure Optimization Based Attribute-aware for Recommendation

Longtao Wang, Qingtian Zeng, Guiyuan Yuan, Hua Duan, Cheng Cheng, Zilong Wang

ICASSP 2025accepted

Heterogeneous Graph Neural Networks(HGNNs) are widely regarded as an effective tool for modeling data with graph structures in recommendation. Current research lacks modeling of user attribute and project attribute distribution preferences, limiting graph structure optimization potential. In respons…

Cited by 0 Source ↗Scholar ↗