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István Pilászy
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2010 – 2019
- 2013
- [c7]Bottyán Németh, Gábor Takács, István Pilászy, Domonkos Tikk:
Visualization of movie features in collaborative filtering. SoMeT 2013: 229-233 - 2011
- [c6]Gábor Takács, István Pilászy, Domonkos Tikk:
Applications of the conjugate gradient method for implicit feedback collaborative filtering. RecSys 2011: 297-300 - 2010
- [b1]István Pilászy:
Faktorizáció alapú nagy léptékű ajánló algoritmusok. Budapest University of Technology and Economics, Hungary, 2010 - [c5]István Pilászy, Dávid Zibriczky, Domonkos Tikk:
Fast als-based matrix factorization for explicit and implicit feedback datasets. RecSys 2010: 71-78
2000 – 2009
- 2009
- [j2]Gábor Takács, István Pilászy, Bottyán Németh, Domonkos Tikk:
Scalable Collaborative Filtering Approaches for Large Recommender Systems. J. Mach. Learn. Res. 10: 623-656 (2009) - [c4]István Pilászy, Domonkos Tikk:
Computational Complexity Reduction for Factorization-Based Collaborative Filtering Algorithms. EC-Web 2009: 229-239 - [c3]István Pilászy, Domonkos Tikk:
Recommending new movies: even a few ratings are more valuable than metadata. RecSys 2009: 93-100 - 2008
- [c2]Gábor Takács, István Pilászy, Bottyán Németh, Domonkos Tikk:
Investigation of Various Matrix Factorization Methods for Large Recommender Systems. ICDM Workshops 2008: 553-562 - [c1]Gábor Takács, István Pilászy, Bottyán Németh, Domonkos Tikk:
Matrix factorization and neighbor based algorithms for the netflix prize problem. RecSys 2008: 267-274 - 2007
- [j1]Gábor Takács, István Pilászy, Bottyán Németh, Domonkos Tikk:
Major components of the gravity recommendation system. SIGKDD Explor. 9(2): 80-83 (2007)
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