Követés
Balázs Hidasi
Balázs Hidasi
Gravity R&D
E-mail megerősítve itt: gravityrd.com - Kezdőlap
Cím
Hivatkozott rá
Hivatkozott rá
Év
Session-based recommendations with recurrent neural networks
B Hidasi, A Karatzoglou, L Baltrunas, D Tikk
arXiv preprint arXiv:1511.06939, 2015
32442015
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
1167*2016
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
8622018
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
7422017
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
5522016
Fast ALS-based tensor factorization for context-aware recommendation from implicit feedback
B Hidasi, D Tikk
Machine Learning and Knowledge Discovery in Databases: European Conference …, 2012
1882012
General factorization framework for context-aware recommendations
B Hidasi, D Tikk
Data Mining and Knowledge Discovery, 1-30, 2015
1172015
Deep learning for recommender systems
A Karatzoglou, B Hidasi
Proceedings of the eleventh ACM conference on recommender systems, 396-397, 2017
1092017
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
602016
Multimedia recommender systems: Algorithms and challenges
Y Deldjoo, M Schedl, B Hidasi, Y Wei, X He
Recommender systems handbook, 973-1014, 2021
562021
Initializing Matrix Factorization Methods on Implicit Feedback Databases.
B Hidasi, D Tikk
J. Univers. Comput. Sci. 19 (12), 1834-1853, 2013
222013
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
202016
Multimedia recommender systems
Y Deldjoo, M Schedl, B Hidasi, P Knees
Proceedings of the 12th ACM Conference on Recommender Systems, 537-538, 2018
172018
Speeding up ALS learning via approximate methods for context-aware recommendations
B Hidasi, D Tikk
Knowledge and Information Systems, 1-25, 2015
162015
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
162012
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
162012
ShiftTree: An interpretable model-based approach for time series classification
B Hidasi, C Gáspár-Papanek
Machine Learning and Knowledge Discovery in Databases: European Conference …, 2011
162011
Factorization models for context-aware recommendations
B Hidasi
Infocommun J VI (4), 27-34, 2014
132014
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
132013
Neighbor methods vs. matrix factorization—Case studies of real-life recommendations
I Pilászy, A Serény, G Dózsa, B Hidasi, A Sári, J Gub
LSRS Workshop at ACM RecSys, 2015
122015
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Cikkek 1–20