| Recommendation as language processing (rlp): A unified pretrain, personalized prompt & predict paradigm (p5) S Geng, S Liu, Z Fu, Y Ge, Y Zhang Proceedings of the 16th ACM Conference on Recommender Systems, 299-315, 2022 | 359 | 2022 |
| User-oriented fairness in recommendation Y Li, H Chen, Z Fu, Y Ge, Y Zhang Proceedings of the web conference 2021, 624-632, 2021 | 239 | 2021 |
| Fairness-aware explainable recommendation over knowledge graphs Z Fu, Y Xian, R Gao, J Zhao, Q Huang, Y Ge, S Xu, S Geng, C Shah, ... Proceedings of the 43rd international ACM SIGIR conference on research and …, 2020 | 238 | 2020 |
| Towards long-term fairness in recommendation Y Ge, S Liu, R Gao, Y Xian, Y Li, X Zhao, C Pei, F Sun, J Ge, W Ou, ... Proceedings of the 14th ACM international conference on web search and data …, 2021 | 217 | 2021 |
| Understanding echo chambers in e-commerce recommender systems Y Ge, S Zhao, H Zhou, C Pei, F Sun, W Ou, Y Zhang Proceedings of the 43rd international ACM SIGIR conference on research and …, 2020 | 158 | 2020 |
| Openagi: When llm meets domain experts Y Ge, W Hua, K Mei, J Tan, S Xu, Z Li, Y Zhang Advances in Neural Information Processing Systems 36, 2024 | 153 | 2024 |
| Counterfactual explainable recommendation J Tan, S Xu, Y Ge, Y Li, X Chen, Y Zhang Proceedings of the 30th ACM International Conference on Information …, 2021 | 153 | 2021 |
| Towards personalized fairness based on causal notion Y Li, H Chen, S Xu, Y Ge, Y Zhang Proceedings of the 44th International ACM SIGIR Conference on Research and …, 2021 | 137 | 2021 |
| Learning and evaluating graph neural network explanations based on counterfactual and factual reasoning J Tan, S Geng, Z Fu, Y Ge, S Xu, Y Li, Y Zhang Proceedings of the ACM web conference 2022, 1018-1027, 2022 | 102 | 2022 |
| CAFE: Coarse-to-fine neural symbolic reasoning for explainable recommendation Y Xian, Z Fu, H Zhao, Y Ge, X Chen, Q Huang, S Geng, Z Qin, G De Melo, ... Proceedings of the 29th ACM International Conference on Information …, 2020 | 100 | 2020 |
| Path language modeling over knowledge graphsfor explainable recommendation S Geng, Z Fu, J Tan, Y Ge, G De Melo, Y Zhang Proceedings of the ACM Web Conference 2022, 946-955, 2022 | 78 | 2022 |
| Fairness in recommendation: A survey Y Li, H Chen, S Xu, Y Ge, J Tan, S Liu, Y Zhang arXiv preprint arXiv:2205.13619, 2022 | 74 | 2022 |
| How to index item ids for recommendation foundation models W Hua, S Xu, Y Ge, Y Zhang Proceedings of the Annual International ACM SIGIR Conference on Research and …, 2023 | 71 | 2023 |
| Toward Pareto Efficient Fairness-Utility Trade-off in Recommendation through Reinforcement Learning Y Ge, X Zhao, L Yu, S Paul, D Hu, CC Hsieh, Y Zhang arXiv preprint arXiv:2201.00140, 2022 | 70 | 2022 |
| Explainable fairness in recommendation Y Ge, J Tan, Y Zhu, Y Xia, J Luo, S Liu, Z Fu, S Geng, Z Li, Y Zhang Proceedings of the 45th International ACM SIGIR Conference on Research and …, 2022 | 63 | 2022 |
| A survey on trustworthy recommender systems Y Ge, S Liu, Z Fu, J Tan, Z Li, S Xu, Y Li, Y Xian, Y Zhang ACM Transactions on Recommender Systems, 2022 | 62 | 2022 |
| Tutorial on fairness of machine learning in recommender systems Y Li, Y Ge, Y Zhang Proceedings of the 44th international ACM SIGIR conference on research and …, 2021 | 59 | 2021 |
| Causal collaborative filtering S Xu, Y Ge, Y Li, Z Fu, X Chen, Y Zhang Proceedings of the 2023 ACM SIGIR International Conference on Theory of …, 2023 | 57 | 2023 |
| sharedcharging: Data-driven shared charging for large-scale heterogeneous electric vehicle fleets G Wang, W Li, J Zhang, Y Ge, Z Fu, F Zhang, Y Wang, D Zhang Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous …, 2019 | 56 | 2019 |
| War and peace (waragent): Large language model-based multi-agent simulation of world wars W Hua, L Fan, L Li, K Mei, J Ji, Y Ge, L Hemphill, Y Zhang arXiv preprint arXiv:2311.17227, 2023 | 47 | 2023 |
Yongfeng ZhangProfessor of Computer Science, Rutgers UniversityZweryfikowany adres z rutgers.edu
