| Session-based recommendation with graph neural networks S Wu, Y Tang, Y Zhu, L Wang, X Xie, T Tan Proceedings of the AAAI conference on artificial intelligence 33 (01), 346-353, 2019 | 1492 | 2019 |
| Predicting the next location: A recurrent model with spatial and temporal contexts Q Liu, S Wu, L Wang, T Tan Proceedings of the AAAI conference on artificial intelligence 30 (1), 2016 | 958 | 2016 |
| Graph contrastive learning with adaptive augmentation Y Zhu, Y Xu, F Yu, Q Liu, S Wu, L Wang Proceedings of the Web Conference 2021, 2069-2080, 2021 | 827 | 2021 |
| Deep graph contrastive representation learning Y Zhu, Y Xu, F Yu, Q Liu, S Wu, L Wang ICML 2020 Workshop on Graph Representation Learning and Beyond, 2020 | 701 | 2020 |
| A dynamic recurrent model for next basket recommendation F Yu, Q Liu, S Wu, L Wang, T Tan Proceedings of the 39th International ACM SIGIR conference on Research and …, 2016 | 524 | 2016 |
| A Convolutional Approach for Misinformation Identification. F Yu, Q Liu, S Wu, L Wang, T Tan IJCAI, 3901-3907, 2017 | 399 | 2017 |
| A comprehensive survey on cross-modal retrieval K Wang, Q Yin, W Wang, S Wu, L Wang arXiv preprint arXiv:1607.06215, 2016 | 342 | 2016 |
| Every document owns its structure: Inductive text classification via graph neural networks Y Zhang, X Yu, Z Cui, S Wu, Z Wen, L Wang Proceedings of the 58th Annual Meeting of the Association for Computational …, 2020 | 274 | 2020 |
| Context-aware sequential recommendation Q Liu, S Wu, D Wang, Z Li, L Wang 2016 IEEE 16th International Conference on Data Mining (ICDM), 1053-1058, 2016 | 228 | 2016 |
| TAGNN: Target attentive graph neural networks for session-based recommendation F Yu, Y Zhu, Q Liu, S Wu, L Wang, T Tan Proceedings of the 43rd international ACM SIGIR conference on research and …, 2020 | 220 | 2020 |
| Fi-gnn: Modeling feature interactions via graph neural networks for ctr prediction Z Li, Z Cui, S Wu, X Zhang, L Wang Proceedings of the 28th ACM international conference on information and …, 2019 | 188 | 2019 |
| A survey on graph structure learning: Progress and opportunities Y Zhu, W Xu, J Zhang, Y Du, J Zhang, Q Liu, C Yang, S Wu arXiv preprint arXiv:2103.03036, 2021 | 184* | 2021 |
| Information-theoretic outlier detection for large-scale categorical data S Wu, S Wang IEEE transactions on knowledge and data engineering 25 (3), 589-602, 2011 | 173 | 2011 |
| Hierarchical graph convolutional networks for semi-supervised node classification F Hu, Y Zhu, S Wu, L Wang, T Tan Proceedings of the Twenty-Eighth International Joint Conference on …, 2019 | 169 | 2019 |
| MV-RNN: A multi-view recurrent neural network for sequential recommendation Q Cui, S Wu, Q Liu, W Zhong, L Wang IEEE Transactions on Knowledge and Data Engineering 32 (2), 317-331, 2018 | 156* | 2018 |
| A convolutional click prediction model Q Liu, F Yu, S Wu, L Wang Proceedings of the 24th ACM international on conference on information and …, 2015 | 151 | 2015 |
| Deepstyle: Learning user preferences for visual recommendation Q Liu, S Wu, L Wang Proceedings of the 40th international acm sigir conference on research and …, 2017 | 143 | 2017 |
| Personalized graph neural networks with attention mechanism for session-aware recommendation M Zhang, S Wu, M Gao, X Jiang, K Xu, L Wang IEEE Transactions on Knowledge and Data Engineering 34 (8), 3946-3957, 2020 | 131* | 2020 |
| Dressing as a whole: Outfit compatibility learning based on node-wise graph neural networks Z Cui, Z Li, S Wu, XY Zhang, L Wang The world wide web conference, 307-317, 2019 | 127 | 2019 |
| Multi-view clustering via pairwise sparse subspace representation Q Yin, S Wu, R He, L Wang Neurocomputing 156, 12-21, 2015 | 126 | 2015 |
Liang WangNational Lab of Pattern RecognitionVerified email at nlpr.ia.ac.cn
