| 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 | 174 | 2020 |
| User-oriented fairness in recommendation Y Li, H Chen, Z Fu, Y Ge, Y Zhang Proceedings of the Web Conference 2021, 624-632, 2021 | 152 | 2021 |
| 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 | 151 | 2021 |
| 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 | 134 | 2022 |
| 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 | 118 | 2020 |
| 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 | 97 | 2021 |
| 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 | 89 | 2021 |
| 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 | 75 | 2020 |
| 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 | 57 | 2022 |
| 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 | 49 | 2019 |
| 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 | 46 | 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 | 44 | 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 | 43 | 2021 |
| 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 | 42 | 2022 |
| 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 | 41 | 2022 |
| 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 | 38 | 2023 |
| Openagi: When llm meets domain experts Y Ge, W Hua, J Ji, J Tan, S Xu, Y Zhang arXiv preprint arXiv:2304.04370, 2023 | 31 | 2023 |
| A survey on trustworthy recommender systems Y Ge, S Liu, Z Fu, J Tan, Z Li, S Xu, Y Li, Y Xian, Y Zhang arXiv preprint arXiv:2207.12515, 2022 | 30 | 2022 |
| Efficient non-sampling knowledge graph embedding Z Li, J Ji, Z Fu, Y Ge, S Xu, C Chen, Y Zhang Proceedings of the Web Conference 2021, 1727-1736, 2021 | 28 | 2021 |
| Learning causal explanations for recommendation S Xu, Y Li, S Liu, Z Fu, Y Ge, X Chen, Y Zhang The 1st International Workshop on Causality in Search and Recommendation, 2021 | 21 | 2021 |
Yongfeng ZhangRutgers University, Computer ScienceVerified email at rutgers.edu
