| On the opportunity of causal learning in recommendation systems: Foundation, estimation, prediction and challenges P Wu*, H Li*, Y Deng, W Hu, Q Dai, Z Dong, J Sun, R Zhang, XH Zhou IJCAI (Survey Track) 2022, 2022 | 51 | 2022 |
| StableDR: Stabilized Doubly Robust Learning for Recommendation on Data Missing Not at Random H Li, C Zheng, P Wu ICLR 2023, 2023 | 30* | 2023 |
| A generalized doubly robust learning framework for debiasing post-click conversion rate prediction Q Dai, H Li, P Wu, Z Dong, XH Zhou, R Zhang, R Zhang, J Sun KDD 2022, 2022 | 29 | 2022 |
| Balancing unobserved confounding with a few unbiased ratings in debiased recommendations H Li, Y Xiao, C Zheng, P Wu WWW 2023, 2023 | 24 | 2023 |
| TDR-CL: Targeted Doubly Robust Collaborative Learning for Debiased Recommendations H Li, Y Lyu, C Zheng, P Wu ICLR 2023, 2022 | 23 | 2022 |
| Multiple Robust Learning for Recommendation H Li*, Q Dai*, Y Li, Y Lyu, Z Dong, P Wu, XH Zhou AAAI 2023, 2022 | 20 | 2022 |
| Causal recommendation: Progresses and future directions W Wang, Y Zhang, H Li, P Wu, F Feng, X He SIGIR (Tutorial) 2023, 3432-3435, 2023 | 16 | 2023 |
| Propensity Matters: Measuring and Enhancing Balancing for Recommendation H Li, Y Xiao, C Zheng, P Wu, P Cui ICML 2023, 2023 | 14 | 2023 |
| Trustworthy Policy Learning under the Counterfactual No-Harm Criterion H Li, C Zheng, Y Cao, Z Geng, Y Liu, P Wu ICML 2023, 2023 | 11 | 2023 |
| Optimal transport for treatment effect estimation H Wang, J Fan, Z Chen, H Li, W Liu, T Liu, Q Dai, Y Wang, Z Dong, ... NeurIPS 2023, 2024 | 10 | 2024 |
| Who should be given incentives? counterfactual optimal treatment regimes learning for recommendation H Li, C Zheng, P Wu, K Kuang, Y Liu, P Cui KDD 2023, 2023 | 9 | 2023 |
| Removing hidden confounding in recommendation: a unified multi-task learning approach H Li, K Wu, C Zheng, Y Xiao, H Wang, Z Geng, F Feng, X He, P Wu NeurIPS 2023, 2024 | 7 | 2024 |
| Pareto Invariant Representation Learning for Multimedia Recommendation S Huang*, H Li*, Q Li, C Zheng, L Liu MM 2023, 2023 | 5 | 2023 |
| Debiased collaborative filtering with kernel-based causal balancing H Li, C Zheng, Y Xiao, P Wu, Z Geng, X Chen, P Cui ICLR 2024 (Spotlight), 2024 | 4 | 2024 |
| MetaCoCo: A new few-shot classification benchmark with spurious correlation M Zhang, H Li, F Wu, K Kuang ICLR 2024, 2024 | 4 | 2024 |
| TransFR: Transferable Federated Recommendation with Pre-trained Language Models H Zhang, H Liu, H Li, Y Li arXiv preprint arXiv:2402.01124, 2024 | 3 | 2024 |
| CounterCLR: Counterfactual contrastive learning with non-random missing data in recommendation J Wang, H Li, C Zhang, D Liang, E Yu, W Ou, W Wang ICDM 2023, 2023 | 3 | 2023 |
| Contrastive balancing representation learning for heterogeneous dose-response curves estimation M Zhu, A Wu, H Li, R Xiong, B Li, X Yang, X Qin, P Zhen, J Guo, F Wu, ... AAAI 2024, 2024 | 1 | 2024 |
| Hierarchical topological ordering with conditional independence test for limited time series A Wu, H Li, K Kuang, K Zhang, F Wu ICML 2024, 2023 | 1 | 2023 |
| Convformer: Revisiting transformer for sequential user modeling H Wang, J Lian, M Wu, H Li, J Fan, W Xu, C Li, X Xie arXiv preprint arXiv:2308.02925, 2023 | 1 | 2023 |
Kun KuangZhejiang UniversityVerified email at zju.edu.cn
