| PRINCE: Provider-side interpretability with counterfactual explanations in recommender systems A Ghazimatin, O Balalau, R Saha Roy, G Weikum Proceedings of the 13th International Conference on Web Search and Data …, 2020 | 95 | 2020 |
| Counterfactual explanations for neural recommenders KH Tran, A Ghazimatin, R Saha Roy Proceedings of the 44th International ACM SIGIR Conference on Research and …, 2021 | 53 | 2021 |
| ELIXIR: Learning from user feedback on explanations to improve recommender models A Ghazimatin, S Pramanik, R Saha Roy, G Weikum Proceedings of the Web Conference 2021, 3850-3860, 2021 | 20 | 2021 |
| Measuring fairness of rankings under noisy sensitive information A Ghazimatin, M Kleindessner, C Russell, Z Abedjan, J Golebiowski Proceedings of the 2022 ACM Conference on Fairness, Accountability, and …, 2022 | 15 | 2022 |
| FAIRY: a framework for understanding relationships between users' actions and their social feeds A Ghazimatin, R Saha Roy, G Weikum Proceedings of the Twelfth ACM International Conference on Web Search and …, 2019 | 14 | 2019 |
| Learning to Un-Rank: quantifying search exposure for users in online communities AJ Biega, A Ghazimatin, H Ferhatosmanoglu, KP Gummadi, G Weikum Proceedings of the 2017 ACM on Conference on Information and Knowledge …, 2017 | 5 | 2017 |
| Team selection for prediction tasks MA Fazli, A Ghazimatin, J Habibi, H Haghshenas Journal of Combinatorial Optimization 31 (2), 743-757, 2016 | 5 | 2016 |
| Explaining recommendations in heterogeneous networks A Ghazimatin Proceedings of the 43rd International ACM SIGIR Conference on Research and …, 2020 | 2 | 2020 |
| Counterfactual Explanations for Neural Recommenders K Hiep Tran, A Ghazimatin, R Saha Roy arXiv e-prints, arXiv: 2105.05008, 2021 | | 2021 |
| Enhancing explainability and scrutability of recommender systems A Ghazimatin Saarländische Universitäts-und Landesbibliothek, 2021 | | 2021 |
