| Recurrent knowledge graph embedding for effective recommendation Z Sun, J Yang, J Zhang, A Bozzon, LK Huang, C Xu Proceedings of the 12th ACM conference on recommender systems, 297-305, 2018 | 299 | 2018 |
| Librec: A java library for recommender systems. G Guo, J Zhang, Z Sun, N Yorke-Smith UMAP workshops 4, 38-45, 2015 | 244 | 2015 |
| Research commentary on recommendations with side information: A survey and research directions Z Sun, Q Guo, J Yang, H Fang, G Guo, J Zhang, R Burke Electronic Commerce Research and Applications 37, 100879, 2019 | 152 | 2019 |
| Are we evaluating rigorously? benchmarking recommendation for reproducible evaluation and fair comparison Z Sun, D Yu, H Fang, J Yang, X Qu, J Zhang, C Geng Proceedings of the 14th ACM Conference on Recommender Systems, 23-32, 2020 | 124 | 2020 |
| BPRH: Bayesian personalized ranking for heterogeneous implicit feedback H Qiu, Y Liu, G Guo, Z Sun, J Zhang, HT Nguyen Information Sciences 453, 80-98, 2018 | 77 | 2018 |
| An attentional recurrent neural network for personalized next location recommendation Q Guo, Z Sun, J Zhang, YL Theng Proceedings of the AAAI Conference on artificial intelligence 34 (01), 83-90, 2020 | 76 | 2020 |
| Interacting attention-gated recurrent networks for recommendation W Pei, J Yang, Z Sun, J Zhang, A Bozzon, DMJ Tax Proceedings of the 2017 ACM on Conference on Information and Knowledge …, 2017 | 74 | 2017 |
| Attentive Knowledge Graph Embedding for Personalized Recommendation X Sha, Z Sun, J Zhang https://arxiv.org/abs/1910.08288, 2019 | 71* | 2019 |
| Modeling hierarchical category transition for next POI recommendation with uncertain check-ins L Zhang, Z Sun, J Zhang, H Kloeden, F Klanner Information Sciences 515, 169-190, 2020 | 44 | 2020 |
| An Interactive Multi-Task Learning Framework for Next POI Recommendation with Uncertain Check-ins L Zhang, Z Sun, J Zhang, Y Lei, C Li, Z Wu, H Kloeden, F Klanner IJCAI 301 (985), 13954, 2020 | 39 | 2020 |
| Learning hierarchical feature influence for recommendation by recursive regularization J Yang, Z Sun, A Bozzon, J Zhang Proceedings of the 10th ACM Conference on Recommender Systems, 51-58, 2016 | 27 | 2016 |
| Minimalistic attacks: How little it takes to fool deep reinforcement learning policies X Qu, Z Sun, YS Ong, A Gupta, P Wei IEEE Transactions on Cognitive and Developmental Systems 13 (4), 806-817, 2020 | 26 | 2020 |
| MRLR: Multi-level Representation Learning for Personalized Ranking in Recommendation. Z Sun, J Yang, J Zhang, A Bozzon, Y Chen, C Xu IJCAI, 2807-2813, 2017 | 26 | 2017 |
| Exploiting both vertical and horizontal dimensions of feature hierarchy for effective recommendation Z Sun, J Yang, J Zhang, A Bozzon Proceedings of the AAAI Conference on Artificial Intelligence 31 (1), 2017 | 22 | 2017 |
| Aspect-aware point-of-interest recommendation with geo-social influence Q Guo, Z Sun, J Zhang, Q Chen, YL Theng Adjunct Publication of the 25th Conference on User Modeling, Adaptation and …, 2017 | 21 | 2017 |
| Exploiting implicit item relationships for recommender systems Z Sun, G Guo, J Zhang User Modeling, Adaptation and Personalization: 23rd International Conference …, 2015 | 21 | 2015 |
| Tbpr: Trinity preference based bayesian personalized ranking for multivariate implicit feedback H Qiu, G Guo, J Zhang, Z Sun, HT Nguyen, Y Liu Proceedings of the 2016 Conference on User Modeling Adaptation and …, 2016 | 16 | 2016 |
| Exploiting side information for recommendation Q Guo, Z Sun, YL Theng Web Engineering: 19th International Conference, ICWE 2019, Daejeon, South …, 2019 | 13 | 2019 |
| Multi-facet user preference learning for fine-grained item recommendation X Zhou, G Guo, Z Sun, Y Liu Neurocomputing 385, 258-268, 2020 | 12 | 2020 |
| Does Every Data Instance Matter? Enhancing Sequential Recommendation by Eliminating Unreliable Data. Y Sun, B Wang, Z Sun, X Yang IJCAI, 1579-1585, 2021 | 10 | 2021 |
Jie ZhangProfessor of Computer Science and Engineering, Nanyang Technological UniversityGeverifieerd e-mailadres voor ntu.edu.sg
