| Causal incremental graph convolution for recommender system retraining S Ding, F Feng, X He, Y Liao, J Shi, Y Zhang IEEE Transactions on Neural Networks and Learning Systems, 2022 | 38 | 2022 |
| Addressing unmeasured confounder for recommendation with sensitivity analysis S Ding, P Wu, F Feng, Y Wang, X He, Y Liao, Y Zhang Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and …, 2022 | 33 | 2022 |
| Interpolative distillation for unifying biased and debiased recommendation S Ding, F Feng, X He, J Jin, W Wang, Y Liao, Y Zhang Proceedings of the 45th International ACM SIGIR Conference on Research and …, 2022 | 15 | 2022 |
| Be Aware of the Neighborhood Effect: Modeling Selection Bias under Interference H Li, C Zheng, S Ding, P Wu, Z Geng, F Feng, X He arXiv preprint arXiv:2404.19620, 2024 | | 2024 |
| Fairly Recommending with Social Attributes: A Flexible and Controllable Optimization Approach J Jin, H Li, F Feng, S Ding, P Wu, X He Advances in Neural Information Processing Systems 36, 2024 | | 2024 |
| Understanding and Counteracting Feature-Level Bias in Click-Through Rate Prediction J Jin, S Ding, W Wang, F Feng arXiv preprint arXiv:2402.03600, 2024 | | 2024 |
| Be Aware of the Neighborhood Effect: Modeling Selection Bias under Interference H Li, C Zheng, S Ding, P Wu, Z Geng, F Feng, X He The Twelfth International Conference on Learning Representations, 0 | | |
