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Dominik Kowald
Dominik Kowald
Research Area Manager, FAIR-AI, Know-Center, Graz University of Technology
Verified email at tugraz.at - Homepage
Title
Cited by
Cited by
Year
The unfairness of popularity bias in music recommendation: A reproducibility study
D Kowald, M Schedl, E Lex
Advances in Information Retrieval: 42nd European Conference on IR Research …, 2020
1392020
Temporal effects on hashtag reuse in twitter: A cognitive-inspired hashtag recommendation approach
D Kowald, SC Pujari, E Lex
Proceedings of the 26th International Conference on World Wide Web, 1401-1410, 2017
792017
Analyzing item popularity bias of music recommender systems: are different genders equally affected?
O Lesota, A Melchiorre, N Rekabsaz, S Brandl, D Kowald, E Lex, ...
Proceedings of the 15th ACM Conference on Recommender Systems, 601-606, 2021
502021
Psychology-informed recommender systems
E Lex, D Kowald, P Seitlinger, TNT Tran, A Felfernig, M Schedl
Foundations and Trends® in Information Retrieval 15 (2), 134-242, 2021
502021
Recommending tags with a model of human categorization
P Seitlinger, D Kowald, C Trattner, T Ley
Proceedings of the 22nd ACM international conference on Information …, 2013
482013
High Enough? explaining and predicting traveler Satisfaction using airline reviews
E Lacic, D Kowald, E Lex
Proceedings of the 27th ACM Conference on Hypertext and Social Media, 249-254, 2016
452016
Trust-based collaborative filtering: Tackling the cold start problem using regular equivalence
T Duricic, E Lacic, D Kowald, E Lex
Proceedings of the 12th ACM conference on recommender systems, 446-450, 2018
382018
Support the underground: characteristics of beyond-mainstream music listeners
D Kowald, P Muellner, E Zangerle, C Bauer, M Schedl, E Lex
EPJ Data Science 10 (1), 14, 2021
352021
Long time no see: The probability of reusing tags as a function of frequency and recency
D Kowald, P Seitlinger, C Trattner, T Ley
Proceedings of the 23rd International Conference on World Wide Web, 463-468, 2014
332014
Which algorithms suit which learning environments? A comparative study of recommender systems in TEL
S Kopeinik, D Kowald, E Lex
Adaptive and Adaptable Learning: 11th European Conference on Technology …, 2016
322016
The tagrec framework as a toolkit for the development of tag-based recommender systems
D Kowald, S Kopeinik, E Lex
Adjunct publication of the 25th conference on user modeling, adaptation and …, 2017
292017
Tagrec: Towards a standardized tag recommender benchmarking framework
D Kowald, E Lacic, C Trattner
Proceedings of the 25th ACM conference on Hypertext and social media, 305-307, 2014
292014
The influence of frequency, recency and semantic context on the reuse of tags in social tagging systems
D Kowald, E Lex
Proceedings of the 27th ACM Conference on Hypertext and Social Media, 237-242, 2016
282016
Modeling Activation Processes in Human Memory to Predict the Use of Tags in Social Bookmarking Systems.
C Trattner, D Kowald, P Seitlinger, T Ley, S Kopeinik
J. Web Sci. 2 (1), 1-16, 2016
282016
Evaluating tag recommender algorithms in real-world folksonomies: A comparative study
D Kowald, E Lex
Proceedings of the 9th ACM Conference on Recommender Systems, 265-268, 2015
272015
Tackling Cold-Start Users in Recommender Systems with Indoor Positioning Systems.
E Lacic, D Kowald, M Traub, G Luzhnica, J Simon, E Lex
RecSys Posters, 2015
272015
Attention please! a hybrid resource recommender mimicking attention-interpretation dynamics
P Seitlinger, D Kowald, S Kopeinik, I Hasani-Mavriqi, E Lex, T Ley
Proceedings of the 24th International Conference on World Wide Web, 339-345, 2015
262015
Refining frequency-based tag reuse predictions by means of time and semantic context
D Kowald, S Kopeinik, P Seitlinger, T Ley, D Albert, C Trattner
Mining, Modeling, and Recommending'Things' in Social Media: 4th …, 2015
262015
Towards a scalable social recommender engine for online marketplaces: The case of apache solr
E Lacic, D Kowald, D Parra, M Kahr, C Trattner
Proceedings of the 23rd International Conference on World Wide Web, 817-822, 2014
262014
Listener modeling and context-aware music recommendation based on country archetypes
M Schedl, C Bauer, W Reisinger, D Kowald, E Lex
Frontiers in Artificial Intelligence 3, 508725, 2021
252021
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