| 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 | 1020 | 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 | 879 | 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 | 612 | 2020 |
| A comprehensive survey of neighborhood-based recommendation methods X Ning, C Desrosiers, G Karypis recommender system handbook, 2nd edition, 37-76, 2015 | 457 | 2015 |
| Systems and methods for semi-supervised relationship extraction Y Qi, B Bai, X Ning, P Kuksa US Patent 8,874,432, 2014 | 380 | 2014 |
| Sparse Linear Methods with Side Information for Top-N Recommendations X Ning, G Karypis ACM RecSys, 2012 | 202 | 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 | 158 | 2020 |
| Object recognition system with database pruning and querying PK Baheti, A Swaminathan, SD Spindola, X Ning US Patent App. 12/832,796, 2012 | 133 | 2012 |
| 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 | 109 | 2021 |
| Llasmol: Advancing large language models for chemistry with a large-scale, comprehensive, high-quality instruction tuning dataset B Yu, FN Baker, Z Chen, X Ning, H Sun COLM, 2024 | 105 | 2024 |
| Scienceagentbench: Toward rigorous assessment of language agents for data-driven scientific discovery Z Chen, S Chen, Y Ning, Q Zhang, B Wang, B Yu, Y Li, Z Liao, C Wei, Z Lu, ... ICLR, 2025 | 102 | 2025 |
| 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 | 93 | 2021 |
| 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 | 84 | 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 | 59 | 2009 |
| G2Retro as a two-step graph generative models for retrosynthesis prediction Z Chen, OR Ayinde, JR Fuchs, H Sun, X Ning Communications Chemistry 6 (1), 102, 2023 | 58 | 2023 |
| Multi-view learning via probabilistic latent semantic analysis F Zhuang, G Karypis, X Ning, Q He, Z Shi Information Sciences 199, 20-30, 2012 | 57 | 2012 |
| : 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 | 47 | 2021 |
| Multi-task Learning for Recommender System X Ning, G Karypis 2nd Asian Conference on Machine Learning 13, 269--284, 2010 | 47 | 2010 |
| Multi-task Learning for Recommender Systems X Ning, G Karypis | 47 | 2009 |
| ecellm: Generalizing large language models for e-commerce from large-scale, high-quality instruction data B Peng, X Ling, Z Chen, H Sun, X Ning arXiv preprint arXiv:2402.08831, 2024 | 46 | 2024 |
