Domonkos Tikk - Citations Google Scholar

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	Toutes	Depuis 2015
Citations	5765	2959
indice h	35	25
indice i10	82	45

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Coauteurs

Peter BaranyiSzechenyi Istvan University & Budapest University of TechnologyAdresse e-mail validée de sze.hu
Balázs HidasiGravity R&DAdresse e-mail validée de gravityrd.com
Kóczy T. LászlóBME, BudapestAdresse e-mail validée de tmit.bme.hu
Alexandros KaratzoglouGoogleAdresse e-mail validée de google.com
Kevin Kok Wai WongMurdoch University, Perth, Western AustraliaAdresse e-mail validée de murdoch.edu.au
Gábor Takácsassociate professor, Széchenyi István University, Győr, HungaryAdresse e-mail validée de sze.hu
Illés SoltAdresse e-mail validée de gravityrd.com
Ulf LeserKnowledge Management in Bioinformatics, Humboldt-Universität zu BerlinAdresse e-mail validée de informatik.hu-berlin.de
Ron PattonProfessor of Control and Intelligent Systems Engineering, Hull UniversityAdresse e-mail validée de hull.ac.uk
Alan SaidUniversity of GothenburgAdresse e-mail validée de acm.org
Philippe ThomasGerman Research Center for Artificial IntelligenceAdresse e-mail validée de dfki.de
Paolo CremonesiDept. Computer Science, Politecnico di MilanoAdresse e-mail validée de polimi.it
Dávid ZibriczkyDB SchenkerAdresse e-mail validée de finance.bme.hu
Szilveszter KovacsAssociate Professor, University of MiskolcAdresse e-mail validée de iit.uni-miskolc.hu
Martha LarsonRadboud University and Delft University of TechnologyAdresse e-mail validée de tudelft.nl
Linas BaltrunasNetflixAdresse e-mail validée de netflix.com
Jörg HakenbergIllumina, Inc.Adresse e-mail validée de illumina.com
Magyar GáborBudapest University of Technology and EconomicsAdresse e-mail validée de tmit.bme.hu
József K. TarFull Professor, Óbuda University, Bejczy Antal iRobottechnikai KözpontAdresse e-mail validée de nik.uni-obuda.hu
Zsolt Csaba JohanyákJohn von Neumann University, Department of Information TechnologyAdresse e-mail validée de gamf.uni-neumann.hu

Domonkos Tikk

Gravity R&D

Adresse e-mail validée de gravityrd.com

machine learning text mining recommender systems


Titre Trier par citations Trier par année Trier par titre	Citée par Citée par	Année
Session-based recommendations with recurrent neural networks B Hidasi, A Karatzoglou, L Baltrunas, D Tikk arXiv preprint arXiv:1511.06939, 2015	632	2015
Scalable collaborative filtering approaches for large recommender systems G Takács, I Pilászy, B Németh, D Tikk Journal of machine learning research 10 (Mar), 623-656, 2009	525	2009
Major components of the Gravity recommendation system G Takács, I Pilászy, B Németh, D Tikk ACM SIGKDD Explorations Newsletter 9 (2), 80-83, 2007	247	2007
Comprehensive analysis of a new fuzzy rule interpolation method D Tikk, P Baranyi IEEE Transactions on Fuzzy Systems 8 (3), 281-296, 2000	194	2000
From differential equations to PDC controller design via numerical transformation P Baranyi, D Tikk, Y Yam, RJ Patton Computers in Industry 51 (3), 281-297, 2003	186	2003
A comprehensive benchmark of kernel methods to extract protein–protein interactions from literature D Tikk, P Thomas, P Palaga, J Hakenberg, U Leser PLoS computational biology 6 (7), 2010	178	2010
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	176	2016
Investigation of various matrix factorization methods for large recommender systems G Takács, I Pilászy, B Németh, D Tikk 2008 IEEE International Conference on Data Mining Workshops, 553-562, 2008	172	2008
Matrix factorization and neighbor based algorithms for the Netflix prize problem G Takács, I Pilászy, B Németh, D Tikk Proceedings of the 2008 ACM conference on Recommender systems, 267-274, 2008	171	2008
Fast ALS-based matrix factorization for explicit and implicit feedback datasets I Pilászy, D Zibriczky, D Tikk Proceedings of the fourth ACM conference on Recommender systems, 71-78, 2010	167	2010
Recommending new movies: even a few ratings are more valuable than metadata I Pilászy, D Tikk Proceedings of the third ACM conference on Recommender systems, 93-100, 2009	166	2009
Fuzzy rule interpolation for multidimensional input spaces with applications: A case study KW Wong, D Tikk, TD Gedeon, LT Kóczy IEEE transactions on fuzzy systems 13 (6), 809-819, 2005	151	2005
Alternating least squares for personalized ranking G Takács, D Tikk Proceedings of the sixth ACM conference on Recommender systems, 83-90, 2012	130	2012
Fast ALS based tensor factorization for context-aware recommendation from implicit feedback B Hidasi, D Tikk Proceedings of the 2012 European conference on Machine Learning and …, 2012	127	2012
A survey on universal approximation and its limits in soft computing techniques D Tikk, LT Kóczy, TD Gedeon International Journal of Approximate Reasoning 33 (2), 185-202, 2003	127	2003
Stability of interpolative fuzzy KH controllers D Tikk, I Joó, L Kóczy, P Várlaki, B Moser, TD Gedeon Fuzzy Sets and Systems 125 (1), 105-119, 2002	105	2002
Fuzzy rule interpolation Matlab toolbox-FRI toolbox ZC Johanyák, D Tikk, S Kovács, KW Wong 2006 IEEE International Conference on Fuzzy Systems, 351-357, 2006	98	2006
Fuzzy rendszerek LT Kóczy, D Tikk Typotex, 2000	91*	2000
On nowhere denseness of certain fuzzy controllers containing prerestricted number of rules D Tikk Tatra Mountains Math. Publ 16 (1), 369-377, 1999	71*	1999
Relation extraction for drug-drug interactions using ensemble learning P Thomas, M Neves, I Solt, D Tikk, U Leser Training 4 (2,402), 21,425-23,827, 2011	69	2011

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