Author: Domonkos Tikk

Publication Overview

Publication period start: 2012

Number of co-authors: 12

Co-Authors

Number of publications with favourite co-authors

Alan Said

Gábor Takács

István Pilászy

Productive Colleagues

Most productive colleagues in number of publications

Nikos Manouselis

Alan Said

Andreas Hotho

Publications

Takács, Gábor, Pilászy, István, Németh, Bottyán, Tikk, Domonkos (2008): Matrix factorization and neighbor based algorithms for the Netflix prize problem. In: Proceedings of the 2008 ACM Conference on Recommender Systems , 2008, . pp. 267-274. https://dx.doi.org/10.1145/1454008.1454049

Pilászy, István, Tikk, Domonkos (2009): Recommending new movies: even a few ratings are more valuable than metadata. In: Proceedings of the 2009 ACM Conference on Recommender Systems , 2009, . pp. 93-100. https://dx.doi.org/10.1145/1639714.1639731

Pilászy, István, Zibriczky, Dávid, Tikk, Domonkos (2010): Fast ALS-based matrix factorization for explicit and implicit feedback datasets. In: Proceedings of the 2010 ACM Conference on Recommender Systems , 2010, . pp. 71-78. https://dx.doi.org/10.1145/1864708.1864726

Takács, Gábor, Pilászy, István, Tikk, Domonkos (2011): Applications of the conjugate gradient method for implicit feedback collaborative filterin. In: Proceedings of the 2011 ACM Conference on Recommender Systems , 2011, . pp. 297-300. https://dx.doi.org/10.1145/2043932.2043987

Said, Alan, Tikk, Domonkos, Hotho, Andreas (2012): The challenge of recommender systems challenges. In: Proceedings of the 2012 ACM Conference on Recommender Systems , 2012, . pp. 9-10. https://dx.doi.org/10.1145/2365952.2365959

Takács, Gábor, Tikk, Domonkos (2012): Alternating least squares for personalized ranking. In: Proceedings of the 2012 ACM Conference on Recommender Systems , 2012, . pp. 83-90. https://dx.doi.org/10.1145/2365952.2365972

Manouselis, Nikos, Said, Alan, Tikk, Domonkos, Hermanns, Jannis, Kille, Benjamin, Drachsler, Hendrik, Verbert, Katrien, Jack, Kris (2012): Recommender systems challenge 2012. In: Proceedings of the 2012 ACM Conference on Recommender Systems , 2012, . pp. 353-354. https://dx.doi.org/10.1145/2365952.2366043