| DeepFM: a factorization-machine based neural network for CTR prediction H Guo, R Tang, Y Ye, Z Li, X He arXiv preprint arXiv:1703.04247, 2017 | 3260 | 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 | 244 | 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 | 206 | 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 | 190 | 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 | 188 | 2020 |
| 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 | 186 | 2019 |
| 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 | 180 | 2019 |
| Large-scale interactive recommendation with tree-structured policy gradient H Chen, X Dai, H Cai, W Zhang, X Wang, R Tang, Y Zhang, Y Yu Proceedings of the AAAI conference on artificial intelligence 33 (01), 3312-3320, 2019 | 172 | 2019 |
| Towards open-world recommendation with knowledge augmentation from large language models Y Xi, W Liu, J Lin, X Cai, H Zhu, J Zhu, B Chen, R Tang, W Zhang, Y Yu Proceedings of the 18th ACM Conference on Recommender Systems, 12-22, 2024 | 158 | 2024 |
| 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 | 155 | 2018 |
| Deep learning for click-through rate estimation W Zhang, J Qin, W Guo, R Tang, X He arXiv preprint arXiv:2104.10584, 2021 | 129 | 2021 |
| Understanding the planning of LLM agents: A survey X Huang, W Liu, X Chen, X Wang, H Wang, D Lian, Y Wang, R Tang, ... arXiv preprint arXiv:2402.02716, 2024 | 128 | 2024 |
| 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 | 113 | 2021 |
| 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 | 104 | 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 | 101 | 2020 |
| 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 | 89 | 2018 |
| Multi-behavior sequential transformer recommender E Yuan, W Guo, Z He, H Guo, C Liu, R Tang Proceedings of the 45th international ACM SIGIR conference on research and …, 2022 | 81 | 2022 |
| 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 | 80 | 2019 |
| How can recommender systems benefit from large language models: A survey J Lin, X Dai, Y Xi, W Liu, B Chen, H Zhang, Y Liu, C Wu, X Li, C Zhu, ... ACM Transactions on Information Systems 43 (2), 1-47, 2025 | 77 | 2025 |
| 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 | 75 | 2020 |
Huifeng GuoHuawei, Harbin Institute of TechnologyVerified email at huawei.com
