| Fedfast: Going beyond average for faster training of federated recommender systems K Muhammad, Q Wang, D O'Reilly-Morgan, E Tragos, B Smyth, N Hurley, ... Proceedings of the 26th ACM SIGKDD international conference on knowledge …, 2020 | 185 | 2020 |
| Combining rating and review data by initializing latent factor models with topic models for top-n recommendation FJ Peña, D O'Reilly-Morgan, EZ Tragos, N Hurley, E Duriakova, B Smyth, ... Proceedings of the 14th ACM Conference on Recommender Systems, 438-443, 2020 | 28 | 2020 |
| MP4Rec: Explainable and accurate top-n recommendations in heterogeneous information networks MG Ozsoy, D O’Reilly-Morgan, P Symeonidis, EZ Tragos, N Hurley, ... IEEE Access 8, 181835-181847, 2020 | 12 | 2020 |
| Keeping people active and healthy at home using a reinforcement learning-based fitness recommendation framework E Tragos, D O’Reilly-Morgan, J Geraci, B Shi, B Smyth, C Doherty, ... 32nd International Joint Conference on Artificial Intelligence, IJCAI-23 …, 2023 | 1 | 2023 |
| Learning Domain-Independent Representations via Shared Weight Auto-Encoder for Transfer Learning in Recommender Systems Q Wang, D Oàeilly-morgan, EZ Tragos, N Hurley, B Smyth, A Lawlor, ... IEEE Access 10, 71961-71972, 2022 | 1 | 2022 |
| MARF: User-Item Mutual Aware Representation with Feedback Q Wang, K Muhammad, DO Reilly-Morgan, B Smyth, E Tragos, A Lawlor, ... International Conference on Web Engineering, 3-15, 2022 | | 2022 |
| Explainable and Accurate Top-N Recommendations in Heterogeneous Information Networks MG Ozsoy, D O'Reilly-Morgan, P Symeonidis, E Tragos, NJ Hurley, ... IEEE, 2020 | | 2020 |
