| HOT: A height optimized trie index for main-memory database systems R Binna, E Zangerle, M Pichl, G Specht, V Leis Proceedings of the 2018 International Conference on Management of Data, 521-534, 2018 | 103 | 2018 |
| # nowplaying music dataset: Extracting listening behavior from twitter E Zangerle, M Pichl, W Gassler, G Specht Proceedings of the first international workshop on internet-scale multimedia …, 2014 | 102 | 2014 |
| Towards a context-aware music recommendation approach: What is hidden in the playlist name? M Pichl, E Zangerle, G Specht 2015 IEEE international conference on data mining workshop (ICDMW), 1360-1365, 2015 | 81 | 2015 |
| Understanding playlist creation on music streaming platforms M Pichl, E Zangerle, G Specht 2016 IEEE International Symposium on Multimedia (ISM), 475-480, 2016 | 41 | 2016 |
| Improving context-aware music recommender systems: Beyond the pre-filtering approach M Pichl, E Zangerle, G Specht Proceedings of the 2017 ACM on International Conference on Multimedia …, 2017 | 36 | 2017 |
| Combining Spotify and Twitter Data for Generating a Recent and Public Dataset for Music Recommendation. M Pichl, E Zangerle, G Specht Grundlagen von Datenbanken, 35-40, 2014 | 36 | 2014 |
| An empirical evaluation of property recommender systems for Wikidata and collaborative knowledge bases E Zangerle, W Gassler, M Pichl, S Steinhauser, G Specht Proceedings of the 12th International Symposium on Open Collaboration, 1-8, 2016 | 27 | 2016 |
| User models for culture-aware music recommendation: fusing acoustic and cultural cues E Zangerle, M Pichl, M Schedl Transactions of the International Society for Music Information Retrieval 3 (1), 2020 | 21 | 2020 |
| Understanding user-curated playlists on spotify: A machine learning approach M Pichl, E Zangerle, G Specht International Journal of Multimedia Data Engineering and Management (IJMDEM …, 2017 | 20 | 2017 |
| Can Microblogs Predict Music Charts? An Analysis of the Relationship Between# Nowplaying Tweets and Music Charts. E Zangerle, M Pichl, B Hupfauf, G Specht ISMIR, 365-371, 2016 | 18 | 2016 |
| Culture-aware music recommendation E Zangerle, M Pichl, M Schedl Proceedings of the 26th Conference on User Modeling, Adaptation and …, 2018 | 17 | 2018 |
| Mining culture-specific music listening behavior from social media data M Pichl, E Zangerle, G Specht, M Schedl 2017 IEEE International Symposium on Multimedia (ISM), 208-215, 2017 | 17 | 2017 |
| # nowplaying on# Spotify: Leveraging Spotify information on Twitter for artist recommendations M Pichl, E Zangerle, G Specht Current Trends in Web Engineering: 15th International Conference, ICWE 2015 …, 2015 | 15 | 2015 |
| Latent feature combination for multi-context music recommendation M Pichl, E Zangerle 2018 International Conference on Content-Based Multimedia Indexing (CBMI), 1-6, 2018 | 13 | 2018 |
| User models for multi-context-aware music recommendation M Pichl, E Zangerle Multimedia Tools and Applications 80, 22509-22531, 2021 | 12 | 2021 |
| Content-based user models: modeling the many faces of musical preference E Zangerle, M Pichl 19th International Society for Music Information Retrieval conference (ISMIR), 2018 | 11 | 2018 |
| The Many Faces of Users: Modeling Musical Preference. E Zangerle, M Pichl ISMIR, 709-716, 2018 | 5 | 2018 |
| Hierarchical Multilabel Classification and Voting for Genre Classification. B Murauer, M Mayerl, M Tschuggnall, E Zangerle, M Pichl, G Specht MediaEval, 2017 | 4 | 2017 |
| Height Optimized Tries R Binna, E Zangerle, M Pichl, G Specht, V Leis ACM Transactions on Database Systems (TODS) 47 (1), 1-46, 2022 | 1 | 2022 |
| Multi-Context-Aware Recommender Systems: A Study on Music Rfecommendation M Pichl University of Innsbruck, 2018 | 1 | 2018 |
Eva ZangerleUniversität InnsbruckVerified email at uibk.ac.at
