• 2069 Citations
  • 25 h-Index
19972018
If you made any changes in Pure these will be visible here soon.

Fingerprint Dive into the research topics where D. Tikk is active. These topic labels come from the works of this person. Together they form a unique fingerprint.

  • 9 Similar Profiles
Recommender systems Engineering & Materials Science
Factorization Engineering & Materials Science
Interpolation Engineering & Materials Science
Fuzzy rules Engineering & Materials Science
Collaborative filtering Engineering & Materials Science
Feedback Engineering & Materials Science
Tensors Engineering & Materials Science
Fuzzy systems Engineering & Materials Science

Network Recent external collaboration on country level. Dive into details by clicking on the dots.

Research Output 1997 2018

  • 2069 Citations
  • 25 h-Index
  • 62 Conference contribution
  • 26 Article
  • 3 Chapter
  • 1 Paper

DLRS 2018 - Third workshop on deep learning for recommender systems

Hidasi, B., Shapira, B., Karatzoglou, A., Tikk, D., Dieleman, S., Sar-Shalom, O. & Vasile, F., Sep 27 2018, RecSys 2018 - 12th ACM Conference on Recommender Systems. Association for Computing Machinery, Inc, p. 512-513 2 p.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Recommender systems
Learning systems
Deep learning
3 Citations (Scopus)

DLRS 2017 - Second workshop on deep learning for recommender systems

Hidasi, B., Karatzoglou, A., Sar-Shalom, O., DIeleman, S., Shapira, B. & Tikk, D., Aug 27 2017, RecSys 2017 - Proceedings of the 11th ACM Conference on Recommender Systems. Association for Computing Machinery, Inc, p. 370-371 2 p.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Recommender systems
Deep learning
27 Citations (Scopus)

General factorization framework for context-aware recommendations

Hidasi, B. & Tikk, D., Mar 1 2016, In : Data Mining and Knowledge Discovery. 30, 2, p. 342-371 30 p.

Research output: Contribution to journalArticle

Factorization
Feedback
Metadata
Refining
Data structures