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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
1432020
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
812017
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
582021
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
542021
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
402018
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
382021
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
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
292021
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
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
292016
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
292016
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
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
Utilizing online social network and location-based data to recommend products and categories in online marketplaces
E Lacic, D Kowald, L Eberhard, C Trattner, D Parra, LB Marinho
Mining, Modeling, and Recommending'Things' in Social Media: 4th …, 2015
262015
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