| Virtual user approach for group recommender systems using precedence relations VR Kagita, AK Pujari, V Padmanabhan Information Sciences 294, 15-30, 2015 | 58 | 2015 |
| Collaborative filtering using multiple binary maximum margin matrix factorizations V Kumar, AK Pujari, SK Sahu, VR Kagita, V Padmanabhan Information Sciences 380, 1-11, 2017 | 52 | 2017 |
| Group preserving label embedding for multi-label classification V Kumar, AK Pujari, V Padmanabhan, VR Kagita Pattern Recognition 90, 23-34, 2019 | 40 | 2019 |
| Proximal maximum margin matrix factorization for collaborative filtering V Kumar, AK Pujari, SK Sahu, VR Kagita, V Padmanabhan Pattern Recognition Letters 86, 62-67, 2017 | 34 | 2017 |
| Multi-label classification using hierarchical embedding V Kumar, AK Pujari, V Padmanabhan, SK Sahu, VR Kagita Expert Systems with Applications 91, 263-269, 2018 | 30 | 2018 |
| Gp-svm: Tree structured multiclass svm with greedy partitioning SK Sahu, AK Pujari, VR Kagita, V Kumar, V Padmanabhan 2015 International Conference on Information Technology (ICIT), 142-147, 2015 | 21 | 2015 |
| Collaborative filtering by PSO-based MMMF VS Devi, VR Kagita, AK Pujari, V Padmanabhan 2014 IEEE International Conference on Systems, Man, and Cybernetics (SMC …, 2014 | 17 | 2014 |
| Conformal recommender system VR Kagita, AK Pujari, V Padmanabhan, SK Sahu, V Kumar Information Sciences 405, 157-174, 2017 | 16 | 2017 |
| Efficient computation for probabilistic skyline over uncertain preferences AK Pujari, VR Kagita, A Garg, V Padmanabhan Information Sciences 324, 146-162, 2015 | 15 | 2015 |
| Group recommender systems: A virtual user approach based on precedence mining VR Kagita, AK Pujari, V Padmanabhan AI 2013: Advances in Artificial Intelligence: 26th Australasian Joint …, 2013 | 13 | 2013 |
| Precedence mining in group recommender systems VR Kagita, V Padmanabhan, AK Pujari Pattern Recognition and Machine Intelligence: 5th International Conference …, 2013 | 11 | 2013 |
| Committee Selection using Attribute Approvals. VR Kagita, AK Pujari, V Padmanabhan, H Aziz, V Kumar AAMAS, 683-691, 2021 | 9 | 2021 |
| Bi-directional search for skyline probability AK Pujari, VR Kagita, A Garg, V Padmanabhan Algorithms and Discrete Applied Mathematics: First International Conference …, 2015 | 6 | 2015 |
| Bounds on skyline probability for databases with uncertain preferences AK Pujari, V Padmanabhan, VR Kagita International Journal of Approximate Reasoning 80, 199-213, 2017 | 4 | 2017 |
| Threshold-based direct computation of skyline objects for database with uncertain preferences VR Kagita, AK Pujari, V Padmanabhan, V Kumar, SK Sahu PRICAI 2016: Trends in Artificial Intelligence: 14th Pacific Rim …, 2016 | 4 | 2016 |
| A novel social-choice strategy for group modeling in recommender systems VR Kagita, KC Meka, V Padmanabhan 2015 International Conference on Information Technology (ICIT), 153-158, 2015 | 4 | 2015 |
| Greedy partitioning based tree structured multiclass SVM for Odia OCR SK Sahu, AK Pujari, V Kumar, VR Kagita, V Padmanabhan 2015 Fifth National Conference on Computer Vision, Pattern Recognition …, 2015 | 3 | 2015 |
| Inductive conformal recommender system VR Kagita, AK Pujari, V Padmanabhan, V Kumar Knowledge-Based Systems 250, 109108, 2022 | 2 | 2022 |
| Efficient computation of top-k skyline objects in data set with uncertain preferences N Sukhwani, VR Kagita, V Kumar, SK Panda International Journal of Data Warehousing and Mining (IJDWM) 17 (3), 68-80, 2021 | 2 | 2021 |
| Skyline recommendation with uncertain preferences VR Kagita, AK Pujari, V Padmanabhan, V Kumar Pattern Recognition Letters 125, 446-452, 2019 | 2 | 2019 |
Arun PujariUniversity of Hyderabad在 uohyd.ac.in 的电子邮件经过验证
