| Noninvasive self-attention for side information fusion in sequential recommendation C Liu, X Li, G Cai, Z Dong, H Zhu, L Shang Proceedings of the AAAI Conference on Artificial Intelligence 35 (5), 4249-4256, 2021 | 107 | 2021 |
| Improving Ad Click Prediction by Considering Non-displayed Events B Yuan, JY Hsia, MY Yang, H Zhu, CY Chang, Z Dong, CJ Lin Proceedings of the 28th ACM International Conference on Information and …, 2019 | 81 | 2019 |
| 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 | 74 | 2021 |
| Towards Open-World Recommendation with Knowledge Augmentation from Large Language Models Y Xi, W Liu, J Lin, J Zhu, B Chen, R Tang, W Zhang, R Zhang, Y Yu arXiv preprint arXiv:2306.10933, 2023 | 66 | 2023 |
| Less is better: Unweighted data subsampling via influence function Z Wang, H Zhu, Z Dong, X He, SL Huang Proceedings of the AAAI Conference on Artificial Intelligence 34 (04), 6340-6347, 2020 | 50 | 2020 |
| Influence function for unbiased recommendation J Yu, H Zhu, CY Chang, X Feng, B Yuan, X He, Z Dong Proceedings of the 43rd International ACM SIGIR Conference on Research and …, 2020 | 29 | 2020 |
| OptEmbed: Learning Optimal Embedding Table for Click-through Rate Prediction F Lyu, X Tang, H Zhu, H Guo, Y Zhang, R Tang, X Liu Proceedings of the 31st ACM International Conference on Information …, 2022 | 25 | 2022 |
| 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 | 16 | 2020 |
| Estimating True Post-Click Conversion via Group-stratified Counterfactual Inference T Gu, K Kuang, H Zhu, J Li, Z Dong, W Hu, Z Li, X He, Y Liu ADKDD, 2021 | 12 | 2021 |
| One-class Field-aware Factorization Machines for Recommender Systems with Implicit Feedbacks B Yuan, MY Yang, JY Hsia, H Zhu, Z Liu, Z Dong, CJ Lin Technical Report. National Taiwan University, 2019 | 11 | 2019 |
| 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, 2022 | 9 | 2022 |
| Improving Out-of-Distribution Generalization by Adversarial Training with Structured Priors Q Wang, Y Wang, H Zhu, Y Wang NeurIPS 2022, 2022 | 8 | 2022 |
| Collaborative-Enhanced Prediction of Spending on Newly Downloaded Mobile Games under Consumption Uncertainty P Sun, Y Wang, M Zhang, C Wu, Y Fang, H Zhu, Y Fang, M Wang Companion Proceedings of the ACM on Web Conference 2024, 10-19, 2024 | 3 | 2024 |
| 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 |
| Regularization Penalty Optimization for Addressing Data Quality Variance in OoD Algorithms R Yu, H Zhu, K Li, L Hong, R Zhang, N Ye, SL Huang, X He Proceedings of the AAAI Conference on Artificial Intelligence 36 (8), 8945-8953, 2022 | 3 | 2022 |
| Contrastive Multi-view Framework for Customer Lifetime Value Prediction C Wu, J Li, Q Jia, H Zhu, Y Fang, R Tang arXiv preprint arXiv:2306.14400, 2023 | 1 | 2023 |
| Recommendation model training method, recommendation method, apparatus, and computer-readable medium CY Chang, H Zhu, D Zhenhua, X He, Y Bowen US Patent App. 17/242,588, 2021 | 1 | 2021 |
| Confidence-Aware Multi-Field Model Calibration Y Zhao, C Wu, Q Jia, H Zhu, J Yan, L Zong, L Zhang, Z Dong, M Zhang arXiv preprint arXiv:2402.17655, 2024 | | 2024 |
| Robust Long-Tailed Learning via Label-Aware Bounded CVaR H Zhu, R Yu, X Tang, Y Wang, Y Fang, Y Wang arXiv preprint arXiv:2308.15405, 2023 | | 2023 |
Zhenhua DongNoah's ark lab, Huawei Technologies Co., Ltd.在 huawei.com 的电子邮件经过验证
