Home of Data - Databases and Information Systems

We are a subdivision of the Department of Computer Science at the University of Innsbruck.


Our passion lies in developing innovative methods for efficient data storage and analyses aiming to assist users and businesses to meet their information needs.




FWF Project "Humans and Recommender Systems: Towards a Mutual Understanding" Approved

In their last board meeting, the Austrian Science Fund (FWF) approved Eva's stand-alone project proposal titled "Humans and Recommender Systems: Towards a Mutual Understanding" (grant awarded: 599k EUR). In the next three years, we will focus on music recommendations and strive to enhance the understanding of human decision-making underlying the choice of certain music in a given situational context. Furthermore, we aim to advance the users' understanding for the decisions that lead to the recommendation of certain (sequences of) tracks. We believe that an increased understanding and communication between users and the system can contribute to improved user models and, thus, recommendation performance. A previously largely unexplored aspect will be the development of techniques for sequential recommendation strongly targeted at giving explanations for these sequences and considering user feedback.

The project consortium features, besides Eva as principal investigator, Markus Schedl (Johannes Kepler University Linz), Peter Knees (Vienna University of Technology), Marcel Zentner (University of Innsbruck, Department of Psychology), and Michael Huber (University of Music and Performing Arts Vienna).

Support the underground: Characteristics of beyond-mainstream music listeners

Our new study published in EPJ Data Science shows that music recommendations for fans of beyond-mainstream music, such as hard rock and ambient, may receive be less accurate recommendations than for fans of mainstream music, such as pop. Together with Dominik Kowald, Peter Müllner and Elisabeth Lex (TU Graz and Know-Center, Austria), Christine Bauer (University of Utrecht, The Netherlands), and Markus Schedl (Johannes-Kepler-Universität Linz, Austria), Eva has investigated the effects of popularity bias in music recommender systems for listeners of non-mainstream-listeners. Our findings provide valuable insights for developing improved user models and recommendation approaches to better serve beyond-mainstream music listeners. 

Our findings have also been covered by press, among others, by the Austrian newspaper derStandard, Rolling Stone Italia, El Pais (Spain)de Volkskrant (The Netherlands), BioMedCentral, or a blog post by TU Graz, Austria. For a more complete list of news coverage, visit Altmetric.

Article in IEEE Transactions on Affective Computing

Our article titled "Leveraging Affective Hashtags for Ranking Music Recommendations" recently appeared in IEEE Transactions on Affective Computing (impact factor 7.512). Together with Yi-Hsuan Yang (Academia Sinica, Taiwan) and Chih-Ming Chen and Ming-Feng Tsai (both National Chengchi University, Taiwan), Eva extracted affective contextual information from hashtags that music listeners use to describe music on Twitter. The gathered information is modelled as a graph and via state-of-the-art network embedding methods, we learn latent feature representations of users, tracks and hashtags. Based on these representations, we propose eight ranking methods for personalized music recommendations.

Two talks at MediaEval 2020

DBIS is presenting two workshop papers at the MediaEval 2020 on Monday.

At 14:00, we will present our solution to Music mood and theme detiction, and at 16:00 we talk about our solution for detecting conspiracies regarding 5g and Corona.

The registration is free, so feel free to drop in and join the discussions.

MediaEval Participations

This week, we finished two submissions to two challenges organized by the MediaEval benchmarking initiative.

In FakeNews: Corona virus and 5G conspiracy, the task is to classify Twitter messages into three categories, and determine whether they contain assumptions correlating 5G with the outbreak of the Corona virus, or not.

In Emotions and Themes in Music, we predict user-defined tags associated to music tracks by analyzing low-level audio features.

Stay tuned for the results of our approaches in these challenges, as well as our working notes.

Two Papers Accepted at RecSys, ISMIR

Last week, two conference papers co-authored by DBIS member Eva Zangerle have been accepted at conferences:

The paper "Personality Bias of Music Recommendation Algorithms" together with Alessandro Melchiorre and Markus Schedl of JKU Linz has been accepted as a short paper at the 14th ACM Conference on Recommender Systems (acceptance rate for short papers: 20%). The paper "Pandemics, music, and collective sentiment: evidence from the outbreak of COVID-19" together with Meijun Liu and Xiao Hu (both University of Hong Kong) and Alessandro Melchiorre and Markus Schedl (both JKU Linz) has been accepted at the International Society for Music Information Retrieval Conference.

Shared Tasks on Authorship Analysis at PAN 2020

The tasks of the PAN workshop on authorship analyses have been features and presented at the European Conference on Information Retrieval (ECIR) 2020. At PAN, DBIS organizes one of four tasks, the Style Change Detection Task, which is already underway and is still open for new teams and submissions. 

You can find the paper at ECIR in the proceedings of ECIR 2020 (Springer LNCS). 


TISMIR Article on Culture-Aware User Models

Our article "User Models for Culture-Aware Music Recommendation: Fusing Acoustic and Cultural Cues" together with Markus Schedl from JKU Linz, Austria has just been published by the Transactions of the International Society for Music Information Retrieval  journal. In this article, we propose a novel approach to jointly model users by their musical preferences and cultural backgrounds. We describe the musical preferences of users by the acoustic features of the songs the users have listened to and characterize the cultural background of users by culture-related socio-economic features that we infer from the user’s country. 

You can find the article here.

DBIS co-organizes PAN Style Change Detection Task

As in previous years, DBIS members are co-chairs of the PAN Style Change Detection task, co-located with CLEF 2020 (Conference and Labs of the Evaluation Forum).


The goal of the style change detection task is to detect changes in writing style to identify text positions within a given multi-author document at which the author switches. Particularly, given a document, we ask participants to answer the following two questions:

  • Was the given document written by multiple authors? (task 1)
  • For each pair of consecutive paragraphs in the given document: is there a style change between these paragraphs? (task 2)

More information about the task can be found on the task website

International Society for Music Information Retrieval Conference

This week, Eva and Michael are at the 20th International Society for Music Information Retrieval Conference (ISMIR) in Delft, The Netherlands. At the conference, Eva will be a project guide at the WiMIR 2nd Annual Workshop, a satellite event of ISMIR 2019. She also served as co-chair for tutorials for the conference and together with Michael, she will present their paper "Hit Song Prediction: Leveraging Low- and High-level Features".

DBIS at MediaEval 2019

This week, Max is at MediaEval 2019 in Nice, France. MediaEval is a workshop consisting of various challenges in the area of multimedia information retrieval. Together with our partners from Academia Sinica and Taiwan AI Labs, DBIS co-authored two papers on the Emotion and Theme Recognition in Music Using Jamendo task. Max will present one of those papers (Recognizing Song Mood and Theme Using Convolutional Recurrent Neural Networks) on Tuesday.

Günther Specht featured in Standort

Günther Specht is featured in the Standort magazine, where he discusses Building Information Management (BIM) and particularly, our freeBIM project.