| What recommenders recommend: an analysis of recommendation biases and possible countermeasures D Jannach, L Lerche, I Kamehkhosh, M Jugovac User Modeling and User-Adapted Interaction 25, 427-491, 2015 | 221 | 2015 |
| What recommenders recommend–an analysis of accuracy, popularity, and sales diversity effects D Jannach, L Lerche, F Gedikli, G Bonnin User Modeling, Adaptation, and Personalization: 21th International …, 2013 | 148 | 2013 |
| Session-based item recommendation in e-commerce: on short-term intents, reminders, trends and discounts D Jannach, M Ludewig, L Lerche User Modeling and User-Adapted Interaction 27, 351-392, 2017 | 128 | 2017 |
| Adaptation and evaluation of recommendations for short-term shopping goals D Jannach, L Lerche, M Jugovac Proceedings of the 9th ACM Conference on Recommender Systems, 211-218, 2015 | 115 | 2015 |
| Using graded implicit feedback for bayesian personalized ranking L Lerche, D Jannach Proceedings of the 8th ACM Conference on Recommender systems, 353-356, 2014 | 83 | 2014 |
| Beyond" hitting the hits" Generating coherent music playlist continuations with the right tracks D Jannach, L Lerche, I Kamehkhosh Proceedings of the 9th ACM Conference on Recommender Systems, 187-194, 2015 | 73 | 2015 |
| Recommending based on implicit feedback D Jannach, L Lerche, M Zanker Social Information Access: Systems and Technologies, 510-569, 2018 | 72 | 2018 |
| Efficient optimization of multiple recommendation quality factors according to individual user tendencies M Jugovac, D Jannach, L Lerche Expert Systems with Applications 81, 321-331, 2017 | 72 | 2017 |
| On the value of reminders within e-commerce recommendations L Lerche, D Jannach, M Ludewig Proceedings of the 2016 Conference on User Modeling Adaptation and …, 2016 | 57 | 2016 |
| Leveraging multi-dimensional user models for personalized next-track music recommendation D Jannach, I Kamehkhosh, L Lerche Proceedings of the symposium on applied computing, 1635-1642, 2017 | 39 | 2017 |
| Supporting the design of machine learning workflows with a recommendation system D Jannach, M Jugovac, L Lerche ACM Transactions on Interactive Intelligent Systems (TiiS) 6 (1), 1-35, 2016 | 24 | 2016 |
| Item familiarity as a possible confounding factor in user-centric recommender systems evaluation D Jannach, L Lerche, M Jugovac i-com 14 (1), 29-39, 2015 | 15 | 2015 |
| Adaptive recommendation-based modeling support for data analysis workflows D Jannach, M Jugovac, L Lerche Proceedings of the 20th International Conference on Intelligent User …, 2015 | 13 | 2015 |
| Personalized Next-Track Music Recommendation with Multi-dimensional Long-Term Preference Signals. I Kamehkhosh, D Jannach, L Lerche UMAP (Extended Proceedings), 2016 | 11 | 2016 |
| Using implicit feedback for recommender systems: characteristics, applications, and challenges L Lerche | 10 | 2016 |
| Item Familiarity Effects in User-Centric Evaluations of Recommender Systems. D Jannach, L Lerche, M Jugovac RecSys Posters, 2015 | 9 | 2015 |
| Re-ranking recommendations based on predicted short-term interests-a protocol and first experiment D Jannach, L Lerche, M Gdaniec Workshops at the Twenty-Seventh AAAI Conference on Artificial Intelligence, 2013 | 9 | 2013 |
| Offline performance vs. subjective quality experience: a case study in video game recommendation D Jannach, L Lerche Proceedings of the Symposium on Applied Computing, 1649-1654, 2017 | 5 | 2017 |
| Empfehlungssysteme, automatische Erzeugung von Wiedergabelisten und Musikdatenbanken D Jannach, L Lerche, G Bonnin Handbuch Funktionale Musik: Psychologie–Technik–Anwendungsgebiete, 121-157, 2017 | 2 | 2017 |
| Perspektiven in der Offline-Evaluation von Empfehlungsalgorithmen D Jannach, L Lerche HMD Praxis der Wirtschaftsinformatik 50, 34-44, 2013 | 2 | 2013 |
Dietmar JannachUniversity of Klagenfurt, Austria; University of Bergen, NorwayVerified email at aau.at
