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 | 121 | 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 | 114 | 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 | 86 | 2015 |
Understanding playlist creation on music streaming platforms M Pichl, E Zangerle, G Specht 2016 IEEE International Symposium on Multimedia (ISM), 475-480, 2016 | 43 | 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 | 39 | 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 | 29 | 2016 |
User Models for Culture-Aware Music Recommendation: Fusing Acoustic and Cultural Cues. E Zangerle, M Pichl, M Schedl Trans. Int. Soc. Music. Inf. Retr. 3 (1), 1-16, 2020 | 25 | 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 | 24 | 2017 |
Culture-aware music recommendation E Zangerle, M Pichl, M Schedl Proceedings of the 26th Conference on User Modeling, Adaptation and …, 2018 | 21 | 2018 |
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 | 20 | 2016 |
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 | 16 | 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 |
User models for multi-context-aware music recommendation M Pichl, E Zangerle Multimedia Tools and Applications 80 (15), 22509-22531, 2021 | 13 | 2021 |
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 |
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 | 3 | 2022 |
Carl: Sports Award Recommender. M Pichl, B Pichl, E Zangerle eCOM@ SIGIR, 2018 | 2 | 2018 |