Deepfm: A factorization-machine based neural network for CTR prediction H Guo, R Tang, Y Ye, Z Li, X He IJCAI (The most cited paper of IJCAI in the past 5 years), 1725-1731, 2017 | 1395 | 2017 |
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 | 132 | 2018 |
Feature generation by convolutional neural network for click-through rate prediction B Liu, R Tang, Y Chen, J Yu, H Guo, Y Zhang The World Wide Web Conference, 1119-1129, 2019 | 87 | 2019 |
Deep reinforcement learning based recommendation with explicit user-item interactions modeling F Liu, R Tang, X Li, W Zhang, Y Ye, H Chen, H Guo, Y Zhang arXiv preprint arXiv:1810.12027, 2018 | 74 | 2018 |
DSKmeans: a new kmeans-type approach to discriminative subspace clustering X Huang, Y Ye, H Guo, Y Cai, H Zhang, Y Li Knowledge-Based Systems 70, 293-300, 2014 | 58 | 2014 |
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 | 43 | 2019 |
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 | 42 | 2018 |
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 | 39 | 2020 |
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 | 36 | 2020 |
End-to-end deep reinforcement learning based recommendation with supervised embedding F Liu, H Guo, X Li, R Tang, Y Ye, X He Proceedings of the 13th International Conference on Web Search and Data …, 2020 | 26 | 2020 |
PAL: a position-bias aware learning framework for CTR prediction in live recommender systems H Guo, J Yu, Q Liu, R Tang, Y Zhang Proceedings of the 13th ACM Conference on Recommender Systems, 452-456, 2019 | 26 | 2019 |
Graphsail: Graph structure aware incremental learning for recommender systems Y Xu, Y Zhang, W Guo, H Guo, R Tang, M Coates Proceedings of the 29th ACM International Conference on Information …, 2020 | 20 | 2020 |
State representation modeling for deep reinforcement learning based recommendation F Liu, R Tang, X Li, W Zhang, Y Ye, H Chen, H Guo, Y Zhang, X He Knowledge-Based Systems 205, 106170, 2020 | 20 | 2020 |
AutoGroup: Automatic feature grouping for modelling explicit high-order feature interactions in CTR prediction B Liu, N Xue, H Guo, R Tang, S Zafeiriou, X He, Z Li Proceedings of the 43rd International ACM SIGIR conference on research and …, 2020 | 20 | 2020 |
Field-aware probabilistic embedding neural network for ctr prediction W Liu, R Tang, J Li, J Yu, H Guo, X He, S Zhang Proceedings of the 12th ACM Conference on Recommender Systems, 412-416, 2018 | 17 | 2018 |
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 | 16* | 2021 |
Order-aware embedding neural network for CTR prediction W Guo, R Tang, H Guo, J Han, W Yang, Y Zhang Proceedings of the 42nd International ACM SIGIR Conference on Research and …, 2019 | 11 | 2019 |
Dual graph enhanced embedding neural network for ctr prediction W Guo, R Su, R Tan, H Guo, Y Zhang, Z Liu, R Tang, X He Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data …, 2021 | 10 | 2021 |
Top-aware reinforcement learning based recommendation F Liu, R Tang, H Guo, X Li, Y Ye, X He Neurocomputing 417, 255-269, 2020 | 10 | 2020 |
A practical incremental method to train deep ctr models Y Wang, H Guo, R Tang, Z Liu, X He arXiv preprint arXiv:2009.02147, 2020 | 10 | 2020 |