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ORCIDBalázs Hidasi
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2020 – today
- 2024
[c21]Ádám Tibor Czapp
, Mátyás Jani, Bálint Domián, Balázs Hidasi:
Dynamic Product Image Generation and Recommendation at Scale for Personalized E-commerce. RecSys 2024: 768-770- [i9]Ádám Tibor Czapp, Mátyás Jani, Bálint Domián, Balázs Hidasi:
Dynamic Product Image Generation and Recommendation at Scale for Personalized E-commerce. CoRR abs/2408.12392 (2024) - 2023
- [c20]Balázs Hidasi, Ádám Tibor Czapp:
The Effect of Third Party Implementations on Reproducibility. RecSys 2023: 272-282 - [c19]Balázs Hidasi, Ádám Tibor Czapp:
Widespread Flaws in Offline Evaluation of Recommender Systems. RecSys 2023: 848-855 - [i8]Balázs Hidasi, Ádám Tibor Czapp:
Widespread Flaws in Offline Evaluation of Recommender Systems. CoRR abs/2307.14951 (2023) - [i7]Balázs Hidasi, Ádám Tibor Czapp:
The Effect of Third Party Implementations on Reproducibility. CoRR abs/2307.14956 (2023) - 2022
- [r1]Yashar Deldjoo, Markus Schedl, Balázs Hidasi, Yinwei Wei, Xiangnan He:
Multimedia Recommender Systems: Algorithms and Challenges. Recommender Systems Handbook 2022: 973-1014
2010 – 2019
- 2019
- [j4]Cristina Rottondi, Giacomo Verticale, Piero Fraternali, Jasminko Novak, Konstantinos Pelechrinis, Balázs Hidasi, Carmen Karina Vaca Ruiz:
IEEE Access Special Section Editorial: Social Computing Applications for Smart Cities. IEEE Access 7: 65219-65222 (2019) - 2018
- [c18]Balázs Hidasi, Alexandros Karatzoglou:
Recurrent Neural Networks with Top-k Gains for Session-based Recommendations. CIKM 2018: 843-852 - [c17]Balázs Hidasi, Alexandros Karatzoglou, Oren Sar Shalom, Bracha Shapira, Domonkos Tikk, Flavian Vasile, Sander Dieleman:
DLRS 2018: third workshop on deep learning for recommender systems. RecSys 2018: 512-513 - [c16]Yashar Deldjoo, Markus Schedl, Balázs Hidasi, Peter Knees:
Multimedia recommender systems. RecSys 2018: 537-538 - [p1]Balázs Hidasi:
Cutting-Edge Collaborative Recommendation Algorithms: Deep Learning. Collaborative Recommendations 2018: 79-126 - [e3]Balázs Hidasi, Alexandros Karatzoglou, Oren Sar Shalom, Bracha Shapira, Domonkos Tikk, Flavian Vasile, Sander Dieleman:
Proceedings of the 3rd Workshop on Deep Learning for Recommender Systems, DLRS@RecSys 2018, Vancouver, BC, Canada, October 6, 2018. ACM 2018, ISBN 978-1-4503-6617-5 [contents] - 2017
- [c15]Massimo Quadrana, Alexandros Karatzoglou, Balázs Hidasi, Paolo Cremonesi:
Personalizing Session-based Recommendations with Hierarchical Recurrent Neural Networks. RecSys 2017: 130-137 - [c14]Balázs Hidasi, Alexandros Karatzoglou, Oren Sar Shalom, Sander Dieleman, Bracha Shapira, Domonkos Tikk:
DLRS 2017: Second Workshop on Deep Learning for Recommender Systems. RecSys 2017: 370-371 - [c13]Alexandros Karatzoglou, Balázs Hidasi:
Deep Learning for Recommender Systems. RecSys 2017: 396-397 - [e2]Balázs Hidasi, Alexandros Karatzoglou, Oren Sar Shalom, Sander Dieleman, Bracha Shapira, Domonkos Tikk:
Proceedings of the 2nd Workshop on Deep Learning for Recommender Systems, DLRS@RecSys 2017, Como, Italy, August 27, 2017. ACM 2017, ISBN 978-1-4503-5353-3 [contents] - [i6]Balázs Hidasi, Alexandros Karatzoglou:
Recurrent Neural Networks with Top-k Gains for Session-based Recommendations. CoRR abs/1706.03847 (2017) - [i5]Massimo Quadrana, Alexandros Karatzoglou, Balázs Hidasi, Paolo Cremonesi:
Personalizing Session-based Recommendations with Hierarchical Recurrent Neural Networks. CoRR abs/1706.04148 (2017) - 2016
- [b1]Balázs Hidasi:
Context-aware factorization methods for implicit feedback based recommendation problems. Budapest University of Technology and Economics, Hungary, 2016 - [j3]Balázs Hidasi, Domonkos Tikk:
General factorization framework for context-aware recommendations. Data Min. Knowl. Discov. 30(2): 342-371 (2016) - [j2]Balázs Hidasi, Domonkos Tikk:
Speeding up ALS learning via approximate methods for context-aware recommendations. Knowl. Inf. Syst. 47(1): 131-155 (2016) - [c12]Balázs Hidasi, Massimo Quadrana, Alexandros Karatzoglou, Domonkos Tikk:
Parallel Recurrent Neural Network Architectures for Feature-rich Session-based Recommendations. RecSys 2016: 241-248 - [c11]Roberto Pagano, Paolo Cremonesi, Martha A. Larson, Balázs Hidasi, Domonkos Tikk, Alexandros Karatzoglou, Massimo Quadrana:
The Contextual Turn: from Context-Aware to Context-Driven Recommender Systems. RecSys 2016: 249-252 - [c10]Alexandros Karatzoglou, Balázs Hidasi, Domonkos Tikk, Oren Sar Shalom, Haggai Roitman, Bracha Shapira, Lior Rokach:
RecSys'16 Workshop on Deep Learning for Recommender Systems (DLRS). RecSys 2016: 415-416 - [c9]Balázs Hidasi, Alexandros Karatzoglou, Linas Baltrunas, Domonkos Tikk:
Session-based Recommendations with Recurrent Neural Networks. ICLR (Poster) 2016 - [e1]Alexandros Karatzoglou, Balázs Hidasi, Domonkos Tikk, Oren Sar Shalom, Haggai Roitman, Bracha Shapira, Lior Rokach:
Proceedings of the 1st Workshop on Deep Learning for Recommender Systems, DLRS@RecSys 2016, Boston, MA, USA, September 15, 2016. ACM 2016, ISBN 978-1-4503-4795-2 [contents] - [i4]Rami Al-Rfou, Guillaume Alain, Amjad Almahairi, Christof Angermüller, Dzmitry Bahdanau, Nicolas Ballas, Frédéric Bastien, Justin Bayer, Anatoly Belikov, Alexander Belopolsky, Yoshua Bengio, Arnaud Bergeron, James Bergstra, Valentin Bisson, Josh Bleecher Snyder, Nicolas Bouchard, Nicolas Boulanger-Lewandowski, Xavier Bouthillier, Alexandre de Brébisson, Olivier Breuleux, Pierre Luc Carrier, Kyunghyun Cho, Jan Chorowski, Paul F. Christiano, Tim Cooijmans, Marc-Alexandre Côté, Myriam Côté, Aaron C. Courville, Yann N. Dauphin, Olivier Delalleau, Julien Demouth, Guillaume Desjardins, Sander Dieleman, Laurent Dinh, Melanie Ducoffe, Vincent Dumoulin, Samira Ebrahimi Kahou, Dumitru Erhan, Ziye Fan, Orhan Firat, Mathieu Germain, Xavier Glorot, Ian J. Goodfellow, Matthew Graham, Çaglar Gülçehre, Philippe Hamel, Iban Harlouchet, Jean-Philippe Heng, Balázs Hidasi, Sina Honari, Arjun Jain, Sébastien Jean, Kai Jia, Mikhail Korobov, Vivek Kulkarni, Alex Lamb, Pascal Lamblin, Eric Larsen, César Laurent, Sean Lee, Simon Lefrançois, Simon Lemieux, Nicholas Léonard, Zhouhan Lin, Jesse A. Livezey, Cory Lorenz, Jeremiah Lowin, Qianli Ma, Pierre-Antoine Manzagol, Olivier Mastropietro, Robert McGibbon, Roland Memisevic, Bart van Merriënboer, Vincent Michalski, Mehdi Mirza, Alberto Orlandi, Christopher Joseph Pal, Razvan Pascanu, Mohammad Pezeshki, Colin Raffel, Daniel Renshaw, Matthew Rocklin, Adriana Romero, Markus Roth, Peter Sadowski, John Salvatier, François Savard, Jan Schlüter, John Schulman, Gabriel Schwartz, Iulian Vlad Serban, Dmitriy Serdyuk, Samira Shabanian, Étienne Simon, Sigurd Spieckermann, S. Ramana Subramanyam, Jakub Sygnowski, Jérémie Tanguay, Gijs van Tulder, Joseph P. Turian, Sebastian Urban, Pascal Vincent, Francesco Visin, Harm de Vries, David Warde-Farley, Dustin J. Webb, Matthew Willson, Kelvin Xu, Lijun Xue, Li Yao, Saizheng Zhang, Ying Zhang:
Theano: A Python framework for fast computation of mathematical expressions. CoRR abs/1605.02688 (2016) - 2015
- [c8]Benjamin Kille, Fabian Abel, Balázs Hidasi, Sahin Albayrak:
Using Interaction Signals for Job Recommendations. MobiCASE 2015: 301-308 - [c7]Balázs Hidasi:
Context-aware Preference Modeling with Factorization. RecSys 2015: 371-374 - 2014
- [c6]Balázs Hidasi, Domonkos Tikk:
Approximate modeling of continuous context in factorization algorithms. CaRR@ECIR 2014: 3-9 - [i3]Balázs Hidasi, Domonkos Tikk:
General factorization framework for context-aware recommendations. CoRR abs/1401.4529 (2014) - 2013
- [j1]Balázs Hidasi, Domonkos Tikk:
Initializing Matrix Factorization Methods on Implicit Feedback Databases. J. Univers. Comput. Sci. 19(12): 1834-1853 (2013) - [c5]Balázs Hidasi, Domonkos Tikk:
Context-aware item-to-item recommendation within the factorization framework. CaRR@WSDM 2013: 19-25 - [i2]Balázs Hidasi, Domonkos Tikk:
Context-aware recommendations from implicit data via scalable tensor factorization. CoRR abs/1309.7611 (2013) - 2012
- [c4]Balázs Hidasi, Domonkos Tikk:
Enhancing matrix factorization through initialization for implicit feedback databases. CaRR 2012: 2-9 - [c3]Balázs Hidasi, Domonkos Tikk:
Fast ALS-Based Tensor Factorization for Context-Aware Recommendation from Implicit Feedback. ECML/PKDD (2) 2012: 67-82 - [c2]Dávid Zibriczky, Balázs Hidasi, Zoltán Petres, Domonkos Tikk:
Personalized recommendation of linear content on interactive TV platforms: beating the cold start and noisy implicit user feedback. UMAP Workshops 2012 - [i1]Balázs Hidasi, Domonkos Tikk:
Fast ALS-based tensor factorization for context-aware recommendation from implicit feedback. CoRR abs/1204.1259 (2012) - 2011
- [c1]Balázs Hidasi, Csaba Gáspár-Papanek:
ShiftTree: An Interpretable Model-Based Approach for Time Series Classification. ECML/PKDD (2) 2011: 48-64
Coauthor Index
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last updated on 2024-10-23 21:29 CEST by the dblp team
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