| A General Knowledge Distillation Framework for Counterfactual Recommendation via Uniform Data D Liu, P Cheng, Z Dong, X He, W Pan, Z Ming Proceedings of the 43rd International ACM SIGIR Conference on Research and …, 2020 | 165 | 2020 |
| Mitigating Confounding Bias in Recommendation via Information Bottleneck D Liu, P Cheng, H Zhu, Z Dong, X He, W Pan, Z Ming Fifteenth ACM Conference on Recommender Systems, 351-360, 2021 | 69 | 2021 |
| MetaSelector: Meta-Learning for Recommendation with User-Level Adaptive Model Selection M Luo, F Chen, P Cheng, Z Dong, X He, J Feng, Z Li Proceedings of The Web Conference 2020, 2507-2513, 2020 | 51 | 2020 |
| Counterfactual learning for recommender system Z Dong, H Zhu, P Cheng, X Feng, G Cai, X He, J Xu, J Wen Fourteenth ACM Conference on Recommender Systems, 568-569, 2020 | 14 | 2020 |
| KDCRec: Knowledge distillation for counterfactual recommendation via uniform data D Liu, P Cheng, Z Lin, J Luo, Z Dong, X He, W Pan, Z Ming IEEE Transactions on Knowledge and Data Engineering, 2022 | 9 | 2022 |
| Debiased representation learning in recommendation via information bottleneck D Liu, P Cheng, H Zhu, Z Dong, X He, W Pan, Z Ming ACM Transactions on Recommender Systems 1 (1), 1-27, 2023 | 8 | 2023 |
| Bounding system-induced biases in recommender systems with a randomized dataset D Liu, P Cheng, Z Lin, X Zhang, Z Dong, R Zhang, X He, W Pan, Z Ming ACM Transactions on Information Systems 41 (4), 1-26, 2023 | 6 | 2023 |
| DIWIFT: Discovering Instance-wise Influential Features for Tabular Data D Liu, P Cheng, H Zhu, X Tang, Y Chen, X Wang, W Pan, Z Ming, X He Proceedings of the ACM Web Conference 2023, 1673-1682, 2023 | 3 | 2023 |
| Neural network distillation method and apparatus P Cheng, D Zhenhua, X He, X Zhang, S Yin, Y Hu US Patent App. 18/157,277, 2023 | | 2023 |
Xiuqiang HeHKUST, TencentVerified email at tencent.com
