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.
Marta Moscati will present our short paper on "Emotion-Based Music Recommendation from Quality Annotations and Large-Scale User-Generated Tags" co-authored by Marta Moscati, Hannah Strauß, Peer-Ole Jacobsen, Andreas Peintner, Eva Zangerle, Marcel Zentner and Markus Schedl at the 32nd ACM Conference on User Modeling, Adaptation and Personalization next week.
Our paper "Song lyrics have become simpler and more repetitive over the last five decades" was published in Nature Scientific Reports. Our study was also widely reported in the media, for instance in Scientific American, Newsweek, Science, or Der Standard.
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.
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.
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).
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.
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.
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.
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).
This week, the 16th ACM Conference on Recommender Systems takes place and DBIS is part of it :)
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.
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!
Together with Christine Bauer (Utrecht University) and Alan Said (University of Gothenburg), Eva will co-organize the 2nd Workshop on Perspectives on the Evaluation of Recommender Systems at this year's ACM Recommender Systems Conference.
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.
