| Adversarial network embedding Q Dai, Q Li, J Tang, D Wang Thirty-Second AAAI Conference on Artificial Intelligence, 2018 | 267 | 2018 |
| SimpleX: A Simple and Strong Baseline for Collaborative Filtering K Mao, J Zhu, J Wang, Q Dai, Z Dong, X Xiao, X He Proceedings of the 30th ACM International Conference on Information …, 2021 | 167 | 2021 |
| Graph Transfer Learning via Adversarial Domain Adaptation with Graph Convolution Q Dai, XM Wu, J Xiao, X Shen, D Wang IEEE Transactions on Knowledge and Data Engineering, 2022 | 140 | 2022 |
| Network Together: Node Classification via Cross-Network Deep Network Embedding X Shen, Q Dai, S Mao, F Chung, KS Choi IEEE Transactions on Neural Networks and Learning Systems, 2020 | 124* | 2020 |
| Adversarial Training Methods for Network Embedding Q Dai, X Shen, L Zhang, Q Li, D Wang The World Wide Web Conference, 329-339, 2019 | 114 | 2019 |
| Bars: Towards open benchmarking for recommender systems J Zhu, Q Dai, L Su, R Ma, J Liu, G Cai, X Xiao, R Zhang Proceedings of the 45th International ACM SIGIR Conference on Research and …, 2022 | 106 | 2022 |
| Adversarial Deep Network Embedding for Cross-Network Node Classification X Shen, Q Dai, F Chung, W Lu, KS Choi Proceedings of the AAAI Conference on Artificial Intelligence 34 (03), 2991-2999, 2020 | 96 | 2020 |
| A Survey on the Memory Mechanism of Large Language Model based Agents Z Zhang, X Bo, C Ma, R Li, X Chen, Q Dai, J Zhu, Z Dong, JR Wen arXiv preprint arXiv:2404.13501, 2024 | 79 | 2024 |
| Top-N Recommendation with Counterfactual User Preference Simulation M Yang, Q Dai, Z Dong, X Chen, X He, J Wang Proceedings of the 30th ACM International Conference on Information …, 2021 | 74 | 2021 |
| 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, 2022 | 72 | 2022 |
| Optimal transport for treatment effect estimation H Wang, J Fan, Z Chen, H Li, W Liu, T Liu, Q Dai, Y Wang, Z Dong, ... Advances in Neural Information Processing Systems 36, 5404-5418, 2023 | 56 | 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 Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and …, 2022 | 54 | 2022 |
| Out-of-distribution Detection with Implicit Outlier Transformation Q Wang, J Ye, F Liu, Q Dai, M Kalander, T Liu, J Hao, B Han arXiv preprint arXiv:2303.05033, 2023 | 51 | 2023 |
| An attention-based model for conversion rate prediction with delayed feedback via post-click calibration Y Su, L Zhang, Q Dai, B Zhang, J Yan, D Wang, Y Bao, S Xu, Y He, W Yan International Joint Conference on Artificial Intelligence-Pacific Rim …, 2020 | 40 | 2020 |
| Multiple robust learning for recommendation H Li, Q Dai, Y Li, Y Lyu, Z Dong, XH Zhou, P Wu Proceedings of the AAAI Conference on Artificial Intelligence 37 (4), 4417-4425, 2023 | 38 | 2023 |
| Personalized knowledge-aware recommendation with collaborative and attentive graph convolutional networks Q Dai, XM Wu, L Fan, Q Li, H Liu, X Zhang, D Wang, G Lin, K Yang Pattern Recognition 128, 108628, 2022 | 38 | 2022 |
| Boosting Deep CTR Prediction with a Plug-and-Play Pre-trainer for News Recommendation Q Liu, J Zhu, Q Dai, X Wu Proceedings of the 29th International Conference on Computational …, 2022 | 31 | 2022 |
| FINAL: Factorized Interaction Layer for CTR Prediction J Zhu, Q Jia, G Cai, Q Dai, J Li, Z Dong, R Tang, R Zhang Proceedings of the 46th International ACM SIGIR Conference on Research and …, 2023 | 30 | 2023 |
| Social attentive deep Q-networks for recommender systems Y Lei, Z Wang, W Li, H Pei, Q Dai IEEE Transactions on Knowledge and Data Engineering 34 (5), 2443-2457, 2020 | 30 | 2020 |
| Metadata-driven Task Relation Discovery for Multi-task Learning. Z Zheng, Y Wang, Q Dai, H Zheng, D Wang IJCAI, 4426-4432, 2019 | 26 | 2019 |
Zhenhua DongNoah's ark lab, Huawei Technologies Co., Ltd.Zweryfikowany adres z huawei.com