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 | 2789 | 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 | 221 | 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 | 185 | 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 | 161 | 2019 |
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 | 159 | 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 | 152 | 2020 |
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 | 150 | 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 | 147 | 2018 |
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 | 138 | 2019 |
Deep learning for click-through rate estimation W Zhang, J Qin, W Guo, R Tang, X He arXiv preprint arXiv:2104.10584, 2021 | 96 | 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 | 88 | 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 | 82 | 2020 |
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 | 80 | 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 | 78 | 2018 |
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 | 69 | 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, ... arXiv preprint arXiv:2306.10933, 2023 | 68 | 2023 |
Dropnas: Grouped operation dropout for differentiable architecture search W Hong, G Li, W Zhang, R Tang, Y Wang, Z Li, Y Yu arXiv preprint arXiv:2201.11679, 2022 | 63 | 2022 |
An efficient and truthful pricing mechanism for team formation in crowdsourcing markets Q Liu, T Luo, R Tang, S Bressan 2015 IEEE International Conference on Communications (ICC), 567-572, 2015 | 63 | 2015 |
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 | 58 | 2020 |
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 | 56 | 2021 |