| CFM: Convolutional Factorization Machines for Context-Aware Recommendation. X Xin, B Chen, X He, D Wang, Y Ding, JM Jose IJCAI 19, 3926-3932, 2019 | 111 | 2019 |
| Adversarial and contrastive variational autoencoder for sequential recommendation Z Xie, C Liu, Y Zhang, H Lu, D Wang, Y Ding Proceedings of the Web Conference 2021, 449-459, 2021 | 70 | 2021 |
| TGCN: Tag graph convolutional network for tag-aware recommendation B Chen, W Guo, R Tang, X Xin, Y Ding, X He, D Wang Proceedings of the 29th ACM International Conference on Information …, 2020 | 32 | 2020 |
| PHD: A probabilistic model of hybrid deep collaborative filtering for recommender systems J Liu, D Wang, Y Ding Asian Conference on machine learning, 224-239, 2017 | 21 | 2017 |
| AIRec: Attentive intersection model for tag-aware recommendation B Chen, Y Ding, X Xin, Y Li, Y Wang, D Wang Neurocomputing 421, 105-114, 2021 | 15 | 2021 |
| SCFM: Social and crowdsourcing factorization machines for recommendation Y Ding, D Wang, X Xin, G Li, D Sun, X Zeng, R Ranjan Applied Soft Computing 66, 548-556, 2018 | 12 | 2018 |
| Decomposed collaborative filtering: Modeling explicit and implicit factors for recommender systems H Chen, X Xin, D Wang, Y Ding Proceedings of the 14th ACM international conference on web search and data …, 2021 | 10 | 2021 |
| Completely heterogeneous federated learning C Liu, Y Yang, X Cai, Y Ding, H Lu arXiv preprint arXiv:2210.15865, 2022 | 7 | 2022 |
| Extracting attentive social temporal excitation for sequential recommendation Y Li, Y Ding, B Chen, X Xin, Y Wang, Y Shi, R Tang, D Wang arXiv preprint arXiv:2109.13539, 2021 | 7 | 2021 |
| Semi-deterministic and contrastive variational graph autoencoder for recommendation Y Ding, Y Shi, B Chen, C Lin, H Lu, J Li, R Tang, D Wang Proceedings of the 30th ACM international conference on information …, 2021 | 6 | 2021 |
| User actions and timestamp based personalized recommendation for e-commerce system C Chen, D Wang, Y Ding 2016 IEEE International Conference on Computer and Information Technology …, 2016 | 6 | 2016 |
| SocialFM: A social recommender system with factorization machines J Zhou, D Wang, Y Ding, L Yin Web-Age Information Management: 17th International Conference, WAIM 2016 …, 2016 | 6 | 2016 |
| FHSM: factored hybrid similarity methods for top-n recommender systems X Xin, D Wang, Y Ding, C Lini Web Technologies and Applications: 18th Asia-Pacific Web Conference, APWeb …, 2016 | 6 | 2016 |
| Scale-aware task message transferring for multi-task learning S Sirejiding, Y Lu, H Lu, Y Ding 2023 IEEE International Conference on Multimedia and Expo (ICME), 1859-1864, 2023 | 5 | 2023 |
| An energy-efficient computing offloading framework for blockchain-enabled video streaming systems S Yuan, J Li, Y Zhu, C Wu, Y Ding GLOBECOM 2022-2022 IEEE Global Communications Conference, 5183-5188, 2022 | 5 | 2022 |
| Adaptive incentivize for cross-silo federated learning in IIoT: A multi-agent reinforcement learning approach S Yuan, B Dong, H Lvy, H Liu, H Chen, C Wu, S Guo, Y Ding, J Li IEEE Internet of Things Journal, 2023 | 4 | 2023 |
| Exploiting long‐term and short‐term preferences and RFID trajectories in shop recommendation Y Ding, D Wang, G Li, D Sun, X Xin, S Qian Software: Practice and Experience 47 (6), 849-865, 2017 | 4 | 2017 |
| Prompt guided transformer for multi-task dense prediction Y Lu, S Sirejiding, Y Ding, C Wang, H Lu IEEE Transactions on Multimedia, 2024 | 3 | 2024 |
| Position-aware subgraph neural networks with data-efficient learning C Liu, Y Yang, Z Xie, H Lu, Y Ding Proceedings of the Sixteenth ACM International Conference on Web Search and …, 2023 | 3 | 2023 |
| Task aligned meta-learning based augmented graph for cold-start recommendation Y Shi, Y Ding, B Chen, Y Huang, R Tang, D Wang arXiv preprint arXiv:2208.05716, 2022 | 2 | 2022 |
