| Session-based recommendations with recurrent neural networks B Hidasi, A Karatzoglou, L Baltrunas, D Tikk arXiv preprint arXiv:1511.06939, 2015 | 1182 | 2015 |
| Theano: A Python framework for fast computation of mathematical expressions R Al-Rfou, G Alain, A Almahairi, C Angermueller, D Bahdanau, N Ballas, ... arXiv e-prints, arXiv: 1605.02688, 2016 | 843* | 2016 |
| Personalizing session-based recommendations with hierarchical recurrent neural networks M Quadrana, A Karatzoglou, B Hidasi, P Cremonesi Proceedings of the Eleventh ACM Conference on Recommender Systems, 130-137, 2017 | 309 | 2017 |
| Recurrent neural networks with top-k gains for session-based recommendations B Hidasi, A Karatzoglou Proceedings of the 27th ACM international conference on information and …, 2018 | 286 | 2018 |
| Parallel recurrent neural network architectures for feature-rich session-based recommendations B Hidasi, M Quadrana, A Karatzoglou, D Tikk Proceedings of the 10th ACM conference on recommender systems, 241-248, 2016 | 278 | 2016 |
| Fast ALS-based tensor factorization for context-aware recommendation from implicit feedback B Hidasi, D Tikk Joint European Conference on Machine Learning and Knowledge Discovery in …, 2012 | 150 | 2012 |
| General factorization framework for context-aware recommendations B Hidasi, D Tikk Data Mining and Knowledge Discovery, 1-30, 2015 | 81 | 2015 |
| Deep learning for recommender systems A Karatzoglou, B Hidasi Proceedings of the eleventh ACM conference on recommender systems, 396-397, 2017 | 51 | 2017 |
| The contextual turn: From context-aware to context-driven recommender systems R Pagano, P Cremonesi, M Larson, B Hidasi, D Tikk, A Karatzoglou, ... Proceedings of the 10th ACM conference on recommender systems, 249-252, 2016 | 32 | 2016 |
| Initializing Matrix Factorization Methods on Implicit Feedback Databases. B Hidasi, D Tikk J. Univers. Comput. Sci. 19 (12), 1834-1853, 2013 | 16 | 2013 |
| Personalized recommendation of linear content on interactive TV platforms: beating the cold start and noisy implicit user feedback. D Zibriczky, B Hidasi, Z Petres, D Tikk UMAP workshops, 2012 | 16 | 2012 |
| Enhancing matrix factorization through initialization for implicit feedback databases B Hidasi, D Tikk Proceedings of the 2nd Workshop on Context-awareness in Retrieval and …, 2012 | 15 | 2012 |
| Speeding up ALS learning via approximate methods for context-aware recommendations B Hidasi, D Tikk Knowledge and Information Systems, 1-25, 2015 | 14 | 2015 |
| Context-aware item-to-item recommendation within the factorization framework B Hidasi, D Tikk Proceedings of the 3rd Workshop on Context-awareness in Retrieval and …, 2013 | 14 | 2013 |
| Factorization models for context-aware recommendations B Hidasi Infocommun J VI (4), 27-34, 2014 | 13 | 2014 |
| RecSys' 16 Workshop on Deep Learning for Recommender Systems (DLRS) A Karatzoglou, B Hidasi, D Tikk, O Sar-Shalom, H Roitman, B Shapira, ... Proceedings of the 10th ACM Conference on Recommender Systems, 415-416, 2016 | 12 | 2016 |
| Multimedia recommender systems Y Deldjoo, M Schedl, B Hidasi, P Knees Proceedings of the 12th ACM Conference on Recommender Systems, 537-538, 2018 | 10 | 2018 |
| Dlrs 2017: Second workshop on deep learning for recommender systems B Hidasi, A Karatzoglou, O Sar-Shalom, S Dieleman, B Shapira, D Tikk Proceedings of the Eleventh ACM Conference on Recommender Systems, 370-371, 2017 | 10 | 2017 |
| ShiftTree: An interpretable model-based approach for time series classification B Hidasi, C Gáspár-Papanek Joint European Conference on Machine Learning and Knowledge Discovery in …, 2011 | 10 | 2011 |
| Neighbor methods vs. matrix factorization—Case studies of real-life recommendations I Pilászy, A Serény, G Dózsa, B Hidasi, A Sári, J Gub Proceedings of the ACM RecSys 15, 2015 | 9 | 2015 |
Domonkos TikkGravity R&DVerified email at gravityrd.com
