| Deep interest network for click-through rate prediction G Zhou, X Zhu, C Song, Y Fan, H Zhu, X Ma, Y Yan, J Jin, H Li, K Gai Proceedings of the 24th ACM SIGKDD international conference on knowledge …, 2018 | 1897 | 2018 |
| Deep Interest Evolution Network for Click-Through Rate Prediction G Zhou, N Mou, Y Fan, Q Pi, W Bian, C Zhou, X Zhu, K Gai AAAI 2019, 2018 | 1063 | 2018 |
| Practice on Long Sequential User Behavior Modeling for Click-Through Rate Prediction Q Pi, W Bian, G Zhou, X Zhu, K Gai KDD 2019, 2019 | 247 | 2019 |
| Search-based User Interest Modeling with Lifelong Sequential Behavior Data for Click-Through Rate Prediction P Qi, X Zhu, G Zhou, Y Zhang, Z Wang, L Ren, Y Fan, K Gai CIKM 2020, 2020 | 213 | 2020 |
| One model to serve all: Star topology adaptive recommender for multi-domain ctr prediction XR Sheng, L Zhao, G Zhou, X Ding, B Dai, Q Luo, S Yang, J Lv, C Zhang, ... Proceedings of the 30th ACM International Conference on Information …, 2021 | 157 | 2021 |
| Lifelong Sequential Modeling with Personalized Memorization for User Response Prediction K Ren, J Qin, Y Fang, W Zhang, L Zheng, W Bian, G Zhou, J Xu, Y Yu, ... SIGIR 2019, 2019 | 129 | 2019 |
| Rocket Launching: A Universal and Efficient Framework for Training Well-Performing Light Net G Zhou, Y Fan, R Cui, W Bian, X Zhu, K Gai AAAI 2018, 4580, 2018 | 129 | 2018 |
| Xdl: an industrial deep learning framework for high-dimensional sparse data B Jiang, C Deng, H Yi, Z Hu, G Zhou, Y Zheng, S Huang, X Guo, D Wang, ... Proceedings of the 1st International Workshop on Deep Learning Practice for …, 2019 | 63 | 2019 |
| CAN: feature co-action network for click-through rate prediction W Bian, K Wu, L Ren, Q Pi, Y Zhang, C Xiao, XR Sheng, YN Zhu, Z Chan, ... Proceedings of the fifteenth ACM international conference on web search and …, 2022 | 59 | 2022 |
| Image Matters: Jointly Train Advertising CTR Model with Image Representation of Ad and User Behavior T Ge, L Zhao, G Zhou, K Chen, S Liu, H Yi, Z Hu, B Liu, P Sun, H Liu, P Yi, ... CIKM 2018, 2017 | 46* | 2017 |
| Cold: Towards the next generation of pre-ranking system Z Wang, L Zhao, B Jiang, G Zhou, X Zhu, K Gai arXiv preprint arXiv:2007.16122, 2020 | 38 | 2020 |
| Real negatives matter: Continuous training with real negatives for delayed feedback modeling S Gu, XR Sheng, Y Fan, G Zhou, X Zhu Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data …, 2021 | 28 | 2021 |
| A Deep Recurrent Survival Model for Unbiased Ranking J Jin, Y Fang, W Zhang, K Ren, G Zhou, J Xu, Y Yu, J Wang, X Zhu, K Gai SIGIR 2020, 2020 | 27 | 2020 |
| CAN: revisiting feature co-action for click-through rate prediction G Zhou, W Bian, K Wu, L Ren, Q Pi, Y Zhang, C Xiao, XR Sheng, N Mou, ... arXiv preprint arXiv:2011.05625, 2020 | 24 | 2020 |
| Multi-behavior self-supervised learning for recommendation J Xu, C Wang, C Wu, Y Song, K Zheng, X Wang, C Wang, G Zhou, K Gai Proceedings of the 46th International ACM SIGIR Conference on Research and …, 2023 | 21 | 2023 |
| Dcaf: a dynamic computation allocation framework for online serving system B Jiang, P Zhang, R Chen, X Luo, Y Yang, G Wang, G Zhou, X Zhu, K Gai arXiv preprint arXiv:2006.09684, 2020 | 14 | 2020 |
| An end-to-end framework for marketing effectiveness optimization under budget constraint Z Yan, S Wang, G Zhou, J Lin, P Jiang arXiv preprint arXiv:2302.04477, 2023 | 9 | 2023 |
| Instant Representation Learning for Recommendation over Large Dynamic Graphs C Wu, C Wang, J Xu, Z Fang, T Gu, C Wang, Y Song, K Zheng, X Wang, ... 2023 IEEE 39th International Conference on Data Engineering (ICDE), 82-95, 2023 | 6 | 2023 |
| Res-embedding for deep learning based click-through rate prediction modeling G Zhou, K Wu, W Bian, X Zhu, K Gai Proceedings of the 1st International Workshop on Deep Learning Practice for …, 2019 | 6 | 2019 |
| Contextual Distillation Model for Diversified Recommendation F Li, X Si, S Tang, D Wang, K Han, B Han, G Zhou, Y Song, H Chen Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and …, 2024 | 5 | 2024 |
Chang ZhouAlibaba Group; Peking University (zhouchang@pku.edu.cn)在 pku.edu.cn 的电子邮件经过验证
