| 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, 2019 | 1004 | 2019 |
| OFA: Unifying Architectures, Tasks, and Modalities Through a Simple Sequence-to-Sequence Learning Framework P Wang, A Yang, R Men, J Lin, S Bai, Z Li, J Ma, C Zhou*, J Zhou, H Yang ICML 2022, 2022 | 900 | 2022 |
| Qwen Technical Report J Bai, S Bai, Y Chu, Z Cui, K Dang, X Deng, Y Fan, W Ge, Y Han, F Huang, ... https://arxiv.org/abs/2309.16609, 2023 | 645 | 2023 |
| CogView: Mastering Text-to-Image Generation via Transformers M Ding, Z Yang, W Hong, W Zheng, C Zhou, D Yin, J Lin, X Zou, Z Shao, ... NeurIPS 2021, 2021 | 597 | 2021 |
| Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond J Bai*, S Bai*, S Yang*, S Wang, S Tan, P Wang, J Lin, C Zhou*, J Zhou arXiv preprint arXiv:2308.12966, 2023 | 568* | 2023 |
| ATRank: An Attention-Based User Behavior Modeling Framework for Recommendation C Zhou, J Bai, J Song, X Liu, Z Zhao, X Chen, J Gao AAAI 2018, 2017 | 332 | 2017 |
| Learning Disentangled Representations for Recommendation J Ma*, C Zhou*, P Cui, H Yang, W Zhu NeurIPS 2019, 2019 | 319 | 2019 |
| AliGraph: A Comprehensive Graph Neural Network Platform R Zhu, K Zhao, H Yang, W Lin, C Zhou, B Ai, Y Li, J Zhou VLDB 2019, 2019 | 280 | 2019 |
| Cognitive Graph for Multi-Hop Reading Comprehension at Scale M Ding, C Zhou, Q Chen, H Yang, J Tang ACL 2019, 2019 | 273 | 2019 |
| Controllable Multi-Interest Framework for Recommendation Y Cen, J Zhang, X Zou, C Zhou, H Yang, J Tang KDD 2020, 2020 | 244 | 2020 |
| Scalable graph embedding for asymmetric proximity C Zhou, Y Liu, X Liu, Z Liu, J Gao AAAI 2017 31 (1), 2017 | 234 | 2017 |
| Are we really making much progress? Revisiting, benchmarking, and refining heterogeneous graph neural networks Q Lv, M Ding, Q Liu, Y Chen, W Feng, S He, C Zhou, J Jiang, Y Dong, ... KDD 2021, 2021 | 231 | 2021 |
| Disentangled Self-Supervision in Sequential Recommenders J Ma, C Zhou, H Yang, P Cui, X Wang, W Zhu KDD 2020, 2020 | 195 | 2020 |
| Understanding Negative Sampling in Graph Representation Learning Z Yang, M Ding, C Zhou, H Yang, J Zhou, J Tang KDD 2020, 2020 | 177 | 2020 |
| M6: A Chinese Multimodal Pretrainer J Lin*, R Men*, A Yang*, C Zhou, M Ding, Y Zhang, P Wang, A Wang, ... https://arxiv.org/abs/2103.00823, 2021 | 157* | 2021 |
| CogLTX: Applying BERT to Long Texts M Ding, C Zhou, H Yang, J Tang NeurIPS 2020, 2020 | 150 | 2020 |
| Contrastive Learning for Debiased Candidate Generation in Large-Scale Recommender Systems C Zhou*, J Ma*, J Zhang*, J Zhou, H Yang KDD 2021, 2021 | 136 | 2021 |
| M6-Rec: Generative Pretrained Language Models are Open-Ended Recommender Systems Z Cui∗, J Ma∗, C Zhou, J Zhou, H Yang https://arxiv.org/abs/2205.08084, 2022 | 132 | 2022 |
| Personalized Bundle List Recommendation J Bai, C Zhou*, J Song, X Qu, W An, Z Li, J Gao WWW 2019, 2019 | 92 | 2019 |
| Scaling Relationship on Learning Mathematical Reasoning with Large Language Models Z Yuan*, H Yuan*, C Li, G Dong, C Tan, C Zhou, J Zhou arXiv preprint arXiv:2308.01825, 2023 | 87 | 2023 |
Jingren ZhouAlibaba Group, Microsoft在 alibaba-inc.com 的电子邮件经过验证
