Combining Spotify and Twitter Data for Generating a Recent and Public Dataset for Music Recommendation. - Martin Pichl; Eva Zangerle; Günther Specht (Grundlagen von Datenbanken, 2014)

#nowplaying Music Dataset: Extracting Listening Behavior from Twitter. - Eva Zangerle; Martin Pichl; Wolfgang Gassler; Günther Specht (WISMM, 2014)

Towards a Context-Aware Music Recommendation Approach: What is Hidden in the Playlist Name? - Martin Pichl; Eva Zangerle; Günther Specht (ICDM Workshops, 2015)

#Nowplaying on #Spotify: Leveraging Spotify Information on Twitter for Artist Recommendations. - Martin Pichl; Eva Zangerle; Günther Specht (ICWE Workshops, 2015)

Understanding Playlist Creation on Music Streaming Platforms. - Martin Pichl; Eva Zangerle; Günther Specht (ISM, 2016)

Can Microblogs Predict Music Charts? An Analysis of the Relationship Between #Nowplaying Tweets and Music Charts. - Eva Zangerle; Martin Pichl; Benedikt Hupfauf; Günther Specht (ISMIR, 2016)

An Empirical Evaluation of Property Recommender Systems for Wikidata and Collaborative Knowledge Bases. - Eva Zangerle; Wolfgang Gassler; Martin Pichl; Stefan Steinhauser; Günther Specht (OpenSym, 2016)

Mining Culture-Specific Music Listening Behavior from Social Media Data. - Martin Pichl; Eva Zangerle; Günther Specht; Markus Schedl (ISM, 2017)

Hierarchical Multilabel Classification and Voting for Genre Classification. - Benjamin Murauer; Maximilian Mayerl; Michael Tschuggnall; Eva Zangerle; Martin Pichl; Günther Specht (MediaEval, 2017)

Improving Context-Aware Music Recommender Systems: Beyond the Pre-filtering Approach. - Martin Pichl; Eva Zangerle; Günther Specht (ICMR, 2017)

Understanding User-Curated Playlists on Spotify: A Machine Learning Approach. - Martin Pichl; Eva Zangerle; Günther Specht (Int. J. Multim. Data Eng. Manag., 2017)

Latent Feature Combination for Multi-Context Music Recommendation. - Martin Pichl; Eva Zangerle (CBMI, 2018)

The Many Faces of Users: Modeling Musical Preference. - Eva Zangerle; Martin Pichl (ISMIR, 2018)

Carl: Sports Award Recommender. - Martin Pichl; Bernward Pichl; Eva Zangerle (eCOM@SIGIR, 2018)

HOT: A Height Optimized Trie Index for Main-Memory Database Systems. - Robert Binna; Eva Zangerle; Martin Pichl; Günther Specht; Viktor Leis (SIGMOD Conference, 2018)

Culture-Aware Music Recommendation. - Eva Zangerle; Martin Pichl; Markus Schedl (UMAP, 2018)

User Models for Culture-Aware Music Recommendation: Fusing Acoustic and Cultural Cues. - Eva Zangerle; Martin Pichl; Markus Schedl (Trans. Int. Soc. Music. Inf. Retr., 2020)

User models for multi-context-aware music recommendation. - Martin Pichl; Eva Zangerle (Multim. Tools Appl., 2021)

Height Optimized Tries. - Robert Binna; Eva Zangerle; Martin Pichl; Günther Specht; Viktor Leis (ACM Trans. Database Syst., 2022)