Cheng Wu

According to our database, Cheng Wu authored at least 16 papers between 2021 and 2025.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2025

Pone-GNN: Integrating Positive and Negative Feedback in Graph Neural Networks for Recommender Systems.

Ziyang Liu, Chaokun Wang, Shuwen Zheng, Cheng Wu, Kai Zheng, Yang Song, Na Mou

Trans. Recomm. Syst., June, 2025

Balancing Self-Presentation and Self-Hiding for Exposure-Aware Recommendation Based on Graph Contrastive Learning.

Leqi Zheng, Chaokun Wang, Ziyang Liu, Canzhi Chen, Cheng Wu, Hongwei Li

Proceedings of the 48th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2025

Effective and Scalable Heterogeneous Graph Neural Network Framework with Convolution-oriented Attention.

Ziqian Zhang, Chaokun Wang, Shuwen Zheng, Cheng Wu, Ziyang Liu, Hao Feng

Proceedings of the 41st IEEE International Conference on Data Engineering, 2025

LSM-Community: A Graph Storage System Exploiting Community Structure in Social Networks.

Songyao Wang, Chaokun Wang, Fang Niu, Cheng Wu

Proceedings of the 41st IEEE International Conference on Data Engineering, 2025

Learning Multiple User Distributions for Recommendation via Guided Conditional Diffusion.

Cheng Wu, Liang Su, Chaokun Wang, Shaoyun Shi, Ziqian Zhang, Ziyang Liu, Wang Peng, Wenjin Wu, Peng Jiang

Proceedings of the AAAI-25, Sponsored by the Association for the Advancement of Artificial Intelligence, February 25, 2025

2024

Enhancing Recommendation Accuracy and Diversity with Box Embedding: A Universal Framework.

Cheng Wu, Shaoyun Shi, Chaokun Wang, Ziyang Liu, Wang Peng, Wenjin Wu, Dongying Kong, Han Li, Kun Gai

Proceedings of the ACM on Web Conference 2024, 2024

Graph Contrastive Learning with Reinforcement Augmentation.

Ziyang Liu, Chaokun Wang, Cheng Wu

Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence, 2024

Temporal Graph Generation Featuring Time-Bound Communities.

Shuwen Zheng, Chaokun Wang, Cheng Wu, Yunkai Lou, Hao Feng, Xuran Yang

Proceedings of the 40th IEEE International Conference on Data Engineering, 2024

Incorporating Dynamic Temperature Estimation into Contrastive Learning on Graphs.

Ziyang Liu, Chaokun Wang, Liqun Yang, Yunkai Lou, Hao Feng, Cheng Wu, Kai Zheng, Yang Song

Proceedings of the 40th IEEE International Conference on Data Engineering, 2024

2023

PANE-GNN: Unifying Positive and Negative Edges in Graph Neural Networks for Recommendation.

Ziyang Liu, Chaokun Wang, Jingcao Xu, Cheng Wu, Kai Zheng, Yang Song, Na Mou, Kun Gai

CoRR, 2023

Multi-behavior Self-supervised Learning for Recommendation.

Jingcao Xu, Chaokun Wang, Cheng Wu, Yang Song, Kai Zheng, Xiaowei Wang, Changping Wang, Guorui Zhou, Kun Gai

Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2023

Graph Contrastive Learning with Generative Adversarial Network.

Cheng Wu, Chaokun Wang, Jingcao Xu, Ziyang Liu, Kai Zheng, Xiaowei Wang, Yang Song, Kun Gai

Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2023

Instant Representation Learning for Recommendation over Large Dynamic Graphs.

Cheng Wu, Chaokun Wang, Jingcao Xu, Ziwei Fang, Tiankai Gu, Changping Wang, Yang Song, Kai Zheng, Xiaowei Wang, Guorui Zhou

Proceedings of the 39th IEEE International Conference on Data Engineering, 2023

2022

HybridGNN: Learning Hybrid Representation in Multiplex Heterogeneous Networks.

Tiankai Gu, Chaokun Wang, Cheng Wu, Jingcao Xu, Yunkai Lou, Changping Wang, Kai Xu, Can Ye, Yang Song

CoRR, 2022

HybridGNN: Learning Hybrid Representation for Recommendation in Multiplex Heterogeneous Networks.

Tiankai Gu, Chaokun Wang, Cheng Wu, Yunkai Lou, Jingcao Xu, Changping Wang, Kai Xu, Can Ye, Yang Song

Proceedings of the 38th IEEE International Conference on Data Engineering, 2022

2021

DiffGNN: Capturing Different Behaviors in Multiplex Heterogeneous Networks for Recommendation.

Tiankai Gu, Chaokun Wang, Cheng Wu

Proceedings of the Artificial Intelligence - First CAAI International Conference, 2021