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.




Special Issue in ACM Transactions on Recommender Systems

We are happy to announce that the Special Issue on Perspectives on Recommender Systems Evaluation (PRSE) co-edited by Eva Zangerle, Christine Bauer, and Alan Said, has just appeared in ACM Transactions on Recommender Systems. It contains ten highly interesting papers that all provide different perspectives on the evaluation of recommender systems - from evaluating the user perspectives, over novel evaluation methods and frameworks, testing recommender systems, to a survey on the current state of RecSys evaluation.

EMMA appeared in Behavior Research Methods

Our paper The Emotion-to-Music Mapping Atlas (EMMA): A systematically organized online database of emotionally evocative music excerpts has just been published in Behavior Research Methods. In this work, we present the Emotion-to-Music Mapping Atlas (EMMA). 364 music excerpts from pop, hip/hop and classical music were rated for felt emotion using the Geneva Emotion Music Scale (GEMS). The sample comprised 517 English- and German-speaking participants and each excerpt was rated by an average of 28.76 participants. This work is part of our ongoing FWF project Humans and Recommender Systems - Towards a Mutual Understanding

ACM Recommender Systems Conference 2023

Team RecSys will have a busy week at the ACM Recommender Systems Conference 2023 in Singapore: on Monday, Amir (paper) and Andreas (paper) will present their works in the doctoral symposium. On Tuesday, Eva (together with Christine Bauer and Alan Said) will hold the third PERSPECTIVES workshop. On Friday, Andreas will present our paper "SPARE: Shortest Path Global Item Relations for Efficient Session-based Recommendation". Furthermore,  Eva and Christine Bauer will present "Evaluating Recommender Systems: Survey and Framework" (paper), published in ACM Computing Surveys, for which they received the Women in RecSys Journal Paper of the Year award (senior category). 

Doctoral Symposium Papers Accepted at RecSys 2023 Conference

We are thrilled to announce that two Doctoral Symposium papers have been accepted for presentation at the prestigious RecSys 2023 Conference in Singapore during September 2023. One paper, authored by Andreas Peintner, plans to contribute significantly to advancing Graph-based Sequential Recommender Systems. The second accepted paper, authored by Amir Reza Mohammadi, promises to make valuable contributions to the development of Explainable Graph Neural Network Recommenders. These notable contributions hold the potential to shape the future of Recommender Systems.




We are very proud that our paper "SPARE: Shortest Path Global Item Relations for Efficient Session-based Recommendation" has just been accepted as a long paper at the ACM Conference on Recommender Systems (RecSys) 2023 in Singapore. In this paper, we propose to model the multi-hop information aggregation mechanism of GNNs over multiple layers via shortest-path edges based on knowledge from the sequential recommendation domain. Additionally, we apply supervised contrastive learning to address the inherent data sparsity.

Keynote at Workshop on Machine Learning for Streaming Media

Eva held a keynote at the Workshop on Machine Learning for Streaming Media at the Web Conference 2023 in Austin. The title of the talk was “What Can We Learn From Analyzing Streaming Music Data?”, detailing the potential of public streaming datasets, example case studies, but also caveats and limitations of using such datasets for research.

Report on the 2nd Workshop on the Perspectives on the Evaluation of Recommender Systems (PERSPECTIVES 2021) at RecSys 2021

Our report on the 2nd Workshop on the Perspectives on the Evaluation of Recommender Systems (PERSPECTIVES 2022) at RecSys 2022 has been published in the December issue of SIGIR Forum.

Music4All-Onion - Resource Paper at ACM CIKM

Last week, Marta Moscati presented our paper "Music4All-Onion -- A Large-Scale Multi-Faceted Content-Centric Music Recommendation Dataset" at the 31st ACM International Conference on Information & Knowledge Management conference. The paper is available here and the dataset and code is publicly available here. This research is part of our FWF project "Humans and Recommender Systems - Towards a Mutual Understanding" (see https://humrec.github.io/ for further information).

Busy Week at the ACM Recommender Systems Conference

This week, the 16th ACM Conference on Recommender Systems takes place and DBIS is part of it :)

  • Eva received the "Women in RecSys: Journal Paper of the Year Awards" for her paper "Leveraging Affective Hashtags for Ranking Music Recommendations" (link), together with Yi-Hsuan Yang, Chih-Ming Chen, and Ming-Feng Tsai. Eva will present the paper in the Women in RecSys session on Wednesday.
  • The 2nd Workshop: Perspectives on the Evaluation of Recommender Systems (co-organized by Eva) will take place on Thursday as a full-day workshop at RecSys.
  • Andreas will present our paper "Unsupervised Graph Embeddings for Session-based Recommendation with Item Features" at CARS: Workshop on Context-Aware Recommender Systems on Friday.

Survey on Recommender Systems Evaluation Published at ACM Computing Surveys

We are very proud that our paper "Evaluating Recommender Systems: Survey and Framework" has just been accepted for publication at ACM Computing Surveys (impact factor 14.324). In this survey paper, Eva and Christine Bauer (Utrecht University) systematically review recommender system evaluation and introduce the “Framework for EValuating Recommender systems” (FEVR) that allows to categorize the evaluation space of recommender systems evaluation. The paper is already available via https://dl.acm.org/doi/10.1145/3556536.

Paper Accepted at ISMIR

Recently, a paper that Max, Eva and Günther authored together with Stefan Brandl and Markus Schedl of the Johannes Kepler Universität Linz was accepted for publication at the ISMIR 2022 conference. The paper is titled Verse Versus Chorus: Structure-aware Feature Extraction for Lyrics-based Genre Recognition, and investigates how the predictive power of lyrical features is impacted by which part of a song they are extracted from.

We look forward to presenting our work at ISMIR 2022 in December!

HOT manuscript appeared in ACM Transactions on Database Systems

Our manuscript on HOT (Height Optimized Tries) has now been published in ACM Transactions on Database Systems (ranked A*). In this article, we present an extended version of our previous SIGMOD paper on HOT - a fast and space-efficient in-memory index structure.

Dataset Paper Accepted for the IJSC special issue on IEEE ISM 2021

This week our our submission "HSP Datasets: Insights on Song Popularity Prediction" has been accepted for the IJSC special issue on IEEE ISM 2021.

Our IJSC journal paper is an extension of our paper "Novel Datasets for Evaluating Song Popularity Prediction Tasks" accepted at the ISM 2021. We extended our publication by providing further experiments and results. This extension is in three directions. First, we now include experiments utilizing Essentia’s mel-band features contained in our dataset. Second, we now additionally include experiments using the Essentia features computed on the short audio samples. Lastly, we extended the number of models used per data source and feature set. We now include a k-nearest neighbor approach for spectral-based features and extend the number of models utilized for predictions based on Mel-spectrograms. Doing so increased the total number of experiments where we report results from 96 to 330.

This also results in a fully reworked Results and Discussion section. We now show how to utilize our results (and subsets of them) to answer a diverse set of research questions regarding song popularity prediction. Based on our baselines, we discuss the best model per feature type. Further, we elaborate on the best hit song prediction approach among the included baseline models. In addition, we shed light on the question if all included measures of popularity are equally hard to predict and finally, we discuss if features extracted from the short audio samples have equal predictive power to those gathered from AcousticBrainz.