| Slim: Sparse linear methods for top-n recommender systems X Ning, G Karypis Data Mining (ICDM), 2011 IEEE 11th International Conference on, 497-506, 2011 | 916 | 2011 |
| Fism: factored item similarity models for top-n recommender systems S Kabbur, X Ning, G Karypis Proceedings of the 19th ACM SIGKDD international conference on Knowledge …, 2013 | 797 | 2013 |
| The National COVID Cohort Collaborative (N3C): Rationale, design, infrastructure, and deployment MA Haendel, CG Chute, TD Bennett, DA Eichmann, J Guinney, WA Kibbe, ... Journal of the American Medical Informatics Association 28 (3), 427-443, 2020 | 455 | 2020 |
| A comprehensive survey of neighborhood-based recommendation methods X Ning, C Desrosiers, G Karypis recommender system handbook, 2nd edition, 37-76, 2015 | 400 | 2015 |
| Systems and methods for semi-supervised relationship extraction Y Qi, B Bai, X Ning, P Kuksa US Patent 8,874,432, 2014 | 361 | 2014 |
| Sparse Linear Methods with Side Information for Top-N Recommendations X Ning, G Karypis ACM RecSys, 2012 | 189 | 2012 |
| Object recognition system with database pruning and querying PK Baheti, A Swaminathan, SD Spindola, X Ning US Patent App. 12/832,796, 2012 | 128 | 2012 |
| DRKG -- drug repurposing knowledge graph for COVID-19 VN Ioannidis, X Song, S Manchanda, M Li, X Pan, D Zheng, X Ning, ... https://github.com/gnn4dr/DRKG/blob/master/DRKG%20Drug%20Repurposing …, 2020 | 111 | 2020 |
| Multi-task Multi-dimensional Hawkes Processes for Modeling Event Sequences D Luo, H Xu, Y Zhen, X Ning, H Zha International Joint Conference of Artificial Intelligence, 2015 | 77 | 2015 |
| Multi-assay-based structure− activity relationship models: improving structure− activity relationship models by incorporating activity information from related targets X Ning, H Rangwala, G Karypis Journal of chemical information and modeling 49 (11), 2444-2456, 2009 | 56 | 2009 |
| A deep generative model for molecule optimization via one fragment modification Z Chen, MR Min, S Parthasarathy, X Ning Nature machine intelligence 3 (12), 1040-1049, 2021 | 54 | 2021 |
| Trust your neighbors: A comprehensive survey of neighborhood-based methods for recommender systems AN Nikolakopoulos, X Ning, C Desrosiers, G Karypis Recommender systems handbook, 39-89, 2021 | 53 | 2021 |
| Multi-view learning via probabilistic latent semantic analysis F Zhuang, G Karypis, X Ning, Q He, Z Shi Information Sciences 199, 20-30, 2012 | 48 | 2012 |
| Multi-task Learning for Recommender System X Ning, G Karypis 2nd Asian Conference on Machine Learning 13, 269--284, 2010 | 48 | 2010 |
| Multi-task Learning for Recommender Systems X Ning, G Karypis | 48 | 2009 |
| : Hybrid Associations Models for Sequential Recommendation B Peng, Z Ren, S Parthasarathy, X Ning IEEE Transactions on Knowledge and Data Engineering 34 (10), 4838-4853, 2021 | 42 | 2021 |
| Trust from the past: Bayesian personalized ranking based link prediction in knowledge graphs B Zhang, S Choudhury, MA Hasan, X Ning, K Agarwal, S Purohit, ... arXiv preprint arXiv:1601.03778, 2016 | 35 | 2016 |
| Grade Prediction with Temporal Course-wise Influence Z Ren, X Ning, H Rangwala 10th International Conference on Educational Data Mining, 48--55, 2017 | 33 | 2017 |
| Improved Machine Learning Models for Predicting Selective Compounds X Ning, M Walters, George journal of chemical informatics and modeling 52 (1), 38–50, 2011 | 31 | 2011 |
| HLAer : a System for Heterogeneous Log Analysis X Ning, G Jiang, H Chen, K Yoshihira SDM workshop on heterogeneous learning, 2014 | 30 | 2014 |
