| A survey of graph neural networks for recommender systems: Challenges, methods, and directions C Gao, Y Zheng, N Li, Y Li, Y Qin, J Piao, Y Quan, J Chang, D Jin, X He, ... ACM Transactions on Recommender Systems 1 (1), 1-51, 2023 | 311* | 2023 |
| Multi-behavior Recommendation with Graph Convolutional Networks B Jin, C Gao, X He, D Jin, Y Li SIGIR, 2020 | 303 | 2020 |
| Disentangling User Interest and Conformity for Recommendation with Causal Embedding Y Zheng, C Gao, X Li, X He, D Jin, Y Li WWW 2021, 2021 | 300 | 2021 |
| Sequential Recommendation with Graph Neural Networks J Chang, C Gao, Y Zheng, Y Hui, Y Niu, Y Song, D Jin, Y Li Proceedings of the 44th International ACM SIGIR Conference on Research and …, 2021 | 283 | 2021 |
| Neural Multi-Task Recommendation from Multi-Behavior Data C Gao, X He, D Gan, X Chen, F Feng, Y Li, TS Chua, D Jin IEEE 35th International Conference on Data Engineering, 2019 | 199 | 2019 |
| Graph neural networks for recommender system C Gao, X Wang, X He, Y Li Proceedings of the Fifteenth ACM International Conference on Web Search and …, 2022 | 185 | 2022 |
| Cross-domain Recommendation Without Sharing User-relevant Data C Gao, X Chen, F Feng, K Zhao, X He, Y Li, D Jin The World Wide Web Conference, 491-502, 2019 | 121 | 2019 |
| Bundle Recommendation with Graph Convolutional Networks J Chang, C Gao, X He, D Jin, Y Li SIGIR, 2020 | 104 | 2020 |
| DGCN: Diversified Recommendation with Graph Convolutional Networks Y Zheng, C Gao, L Chen, D Jin, Y Li WWW 2021, 2021 | 100 | 2021 |
| Learning to recommend with multiple cascading behaviors C Gao, X He, D Gan, X Chen, F Feng, Y Li, TS Chua, L Yao, Y Song, D Jin IEEE transactions on knowledge and data engineering 33 (6), 2588-2601, 2019 | 93 | 2019 |
| S3: Social-network Simulation System with Large Language Model-Empowered Agents C Gao, X Lan, Z Lu, J Mao, J Piao, H Wang, D Jin, Y Li arXiv preprint arXiv:2307.14984, 2023 | 90* | 2023 |
| Price-aware Recommendation with Graph Convolutional Networks Y Zheng, C Gao, X He, Y Li, D Jin IEEE International Conference on Data Engineering (ICDE), 2020 | 81 | 2020 |
| Disentangling Long and Short-Term Interests for Recommendation Y Zheng, C Gao, J Chang, Y Niu, Y Song, D Jin, Y Li Proceedings of the ACM Web Conference 2022, 2256-2267, 2022 | 77 | 2022 |
| Learnable Embedding Sizes for Recommender Systems S Liu, C Gao, Y Chen, D Jin, Y Li International Conference on Learning Representations (ICLR) 2021, 2021 | 74 | 2021 |
| Causal inference in recommender systems: A survey and future directions C Gao, Y Zheng, W Wang, F Feng, X He, Y Li ACM Transactions on Information Systems 42 (4), 1-32, 2024 | 64 | 2024 |
| De-anonymization of mobility trajectories: Dissecting the gaps between theory and practice H Wang, C Gao, Y Li, G Wang, D Jin, J Sun The 25th Annual Network & Distributed System Security Symposium (NDSS’18), 2018 | 60 | 2018 |
| CROSS: Cross-platform Recommendation for Social E-Commerce TH Lin, C Gao, Y Li Proceedings of the 42nd International ACM SIGIR Conference on Research and …, 2019 | 51 | 2019 |
| DPLCF: Differentially Private Local Collaborative Filtering C Gao, C Huang, D Lin, D Jin, Y Li Proceedings of the 43rd International ACM SIGIR Conference on Research and …, 2020 | 50 | 2020 |
| Enhancing Hypergraph Neural Networks with Intent Disentanglement for Session-based Recommendation Y Li, C Gao, H Luo, D Jin, Y Li Proceedings of the 45th International ACM SIGIR Conference on Research and …, 2022 | 47 | 2022 |
| Group-Buying Recommendation for Social E-Commerce J Zhang, C Gao, D Jin, Y Li IEEE International Conference on Data Engineering (ICDE) 2021, 2021 | 46 | 2021 |
Yong LiProfessor, Electronic Engineering, Tsinghua UniversityPatvirtintas el. paštas tsinghua.edu.cn
