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| Federated meta-learning with fast convergence and efficient communication F Chen, M Luo, Z Dong, Z Li, X He arXiv preprint arXiv:1802.07876, 2018 | 588 | 2018 |
| UltraGCN: ultra simplification of graph convolutional networks for recommendation K Mao, J Zhu, X Xiao, B Lu, Z Wang, X He Proceedings of the 30th ACM international conference on information …, 2021 | 357 | 2021 |
| Product-based neural networks for user response prediction over multi-field categorical data Y Qu, B Fang, W Zhang, R Tang, M Niu, H Guo, Y Yu, X He ACM Transactions on Information Systems (TOIS) 37 (1), 1-35, 2018 | 243 | 2018 |
| Autofis: Automatic feature interaction selection in factorization models for click-through rate prediction B Liu, C Zhu, G Li, W Zhang, J Lai, R Tang, X He, Z Li, Y Yu proceedings of the 26th ACM SIGKDD international conference on knowledge …, 2020 | 205 | 2020 |
| A general knowledge distillation framework for counterfactual recommendation via uniform data D Liu, P Cheng, Z Dong, X He, W Pan, Z Ming Proceedings of the 43rd international ACM SIGIR conference on research and …, 2020 | 205 | 2020 |
| Neighbor interaction aware graph convolution networks for recommendation J Sun, Y Zhang, W Guo, H Guo, R Tang, X He, C Ma, M Coates Proceedings of the 43rd international ACM SIGIR conference on research and …, 2020 | 186 | 2020 |
| Interactive recommender system via knowledge graph-enhanced reinforcement learning S Zhou, X Dai, H Chen, W Zhang, K Ren, R Tang, X He, Y Yu Proceedings of the 43rd international ACM SIGIR conference on research and …, 2020 | 185 | 2020 |
| Graph heterogeneous multi-relational recommendation C Chen, W Ma, M Zhang, Z Wang, X He, C Wang, Y Liu, S Ma Proceedings of the AAAI conference on artificial intelligence 35 (5), 3958-3966, 2021 | 180 | 2021 |
| Multi-graph convolution collaborative filtering J Sun, Y Zhang, C Ma, M Coates, H Guo, R Tang, X He 2019 IEEE international conference on data mining (ICDM), 1306-1311, 2019 | 176 | 2019 |
| 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 | 168 | 2021 |
| Open benchmarking for click-through rate prediction J Zhu, J Liu, S Yang, Q Zhang, X He Proceedings of the 30th ACM international conference on information …, 2021 | 138 | 2021 |
| Counterfactual contrastive learning for weakly-supervised vision-language grounding Z Zhang, Z Zhao, Z Lin, X He Advances in Neural Information Processing Systems 33, 18123-18134, 2020 | 137 | 2020 |
| UNBERT: User-News Matching BERT for News Recommendation. Q Zhang, J Li, Q Jia, C Wang, J Zhu, Z Wang, X He IJCAI 21, 3356-3362, 2021 | 136 | 2021 |
| Deep learning for click-through rate estimation W Zhang, J Qin, W Guo, R Tang, X He arXiv preprint arXiv:2104.10584, 2021 | 128 | 2021 |
| An embedding learning framework for numerical features in ctr prediction H Guo, B Chen, R Tang, W Zhang, Z Li, X He Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data …, 2021 | 112 | 2021 |
| A framework for recommending accurate and diverse items using bayesian graph convolutional neural networks J Sun, W Guo, D Zhang, Y Zhang, F Regol, Y Hu, H Guo, R Tang, H Yuan, ... Proceedings of the 26th ACM SIGKDD international conference on knowledge …, 2020 | 101 | 2020 |
| Mitigating confounding bias in recommendation via information bottleneck D Liu, P Cheng, H Zhu, Z Dong, X He, W Pan, Z Ming Proceedings of the 15th ACM conference on Recommender systems, 351-360, 2021 | 97 | 2021 |
| Deepfm: An end-to-end wide & deep learning framework for CTR prediction H Guo, R Tang, Y Ye, Z Li, X He, Z Dong arXiv preprint arXiv:1804.04950, 2018 | 88 | 2018 |
| Regularized two-branch proposal networks for weakly-supervised moment retrieval in videos Z Zhang, Z Lin, Z Zhao, J Zhu, X He Proceedings of the 28th ACM International Conference on Multimedia, 4098-4106, 2020 | 77 | 2020 |
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