| 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 |
| 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 | 88 | 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 | 56 | 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 |
| 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 | 30 | 2023 |
| Hoops: Human-in-the-loop graph reasoning for conversational recommendation Z Fu, Y Xian, Y Zhu, S Xu, Z Li, G De Melo, Y Zhang Proceedings of the 44th International ACM SIGIR Conference on Research and …, 2021 | 30 | 2021 |
| 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 | 29 | 2022 |
| FedCT: Federated collaborative transfer for recommendation S Liu, S Xu, W Yu, Z Fu, Y Zhang, A Marian Proceedings of the 44th international ACM SIGIR conference on research and …, 2021 | 29 | 2021 |
| 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 post-hoc causal explanations for recommendation S Xu, Y Li, S Liu, Z Fu, X Chen, Y Zhang arXiv preprint arXiv:2006.16977, 2020 | 28 | 2020 |
| Deconfounded causal collaborative filtering S Xu, J Tan, S Heinecke, VJ Li, Y Zhang ACM Transactions on Recommender Systems 1 (4), 1-25, 2023 | 22 | 2023 |
| 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 |
| Learning personalized risk preferences for recommendation Y Ge, S Xu, S Liu, Z Fu, F Sun, Y Zhang Proceedings of the 43rd International ACM SIGIR Conference on Research and …, 2020 | 20 | 2020 |
| Fairness-aware federated matrix factorization S Liu, Y Ge, S Xu, Y Zhang, A Marian Proceedings of the 16th ACM Conference on Recommender Systems, 168-178, 2022 | 19 | 2022 |
| Maximizing marginal utility per dollar for economic recommendation Y Ge, S Xu, S Liu, S Geng, Z Fu, Y Zhang The World Wide Web Conference, 2757-2763, 2019 | 18 | 2019 |
| Natural language is all a graph needs R Ye, C Zhang, R Wang, S Xu, Y Zhang arXiv preprint arXiv:2308.07134, 2023 | 17 | 2023 |
| How to Index Item IDs for Recommendation Foundation Models W Hua, S Xu, Y Ge, Y Zhang arXiv preprint arXiv:2305.06569, 2023 | 13 | 2023 |
| Dynamic causal collaborative filtering S Xu, J Tan, Z Fu, J Ji, S Heinecke, Y Zhang Proceedings of the 31st ACM International Conference on Information …, 2022 | 12 | 2022 |
Yongfeng ZhangRutgers University, Computer ScienceVerified email at rutgers.edu
