TU Wien Informatics

Overview Professors Scientific Staff Administrative Staff Student Staff External Lecturers By Responsibilities By Committee By Name
Related
Guest Professors Honoraries Alumni (LinkedIn) #5qw

Peter Knees

Associate Prof. Dipl.-Ing. Dr.techn.

Research Focus

  • Information Systems Engineering: 70%
  • Visual Computing and Human-Centered Technology: 30%

Research Areas

  • Information Retrieval, User interfaces, Music Information Retrieval, Recommender Systems, Artificial Intelligence, Machine Learning
Peter Knees

Roles

  • Associate Professor
    Data Science, E194-04
  • Curriculum Coordinator
    Bachelor / Specialization / Artificial Intelligence + Machine Learning

Contact

2024W

2025S

 

2024

2023

  • Digital Humanism and Norms in Recommender Systems / Prem, E., Neidhardt, J., Knees, P., Woltran, S., & Werthner, H. (2023). Digital Humanism and Norms in Recommender Systems. In S. Vrijenhoek, L. Michiels, J. Kruse, A. Starke, J. Viader Guerrero, & N. Tintarev (Eds.), Proceedings of the First Workshop on the Normative Design and Evaluation of Recommender Systems. CEUR-WS.org. https://doi.org/10.34726/8560
    Download: PDF (191 KB)
    Projects: CDL-RecSys (2022–2028) / HumRec (2021–2025)
  • Leveraging Negative Signals with Self-Attention for Sequential Music Recommendation / Seshadri, P., & Knees, P. (2023). Leveraging Negative Signals with Self-Attention for Sequential Music Recommendation. In Proceedings of the Music Recommender Systems Workshop (MuRS) at the 17th ACM Recommender Systems Conference (RecSys’23). Music Recommender Systems Workshop at the 17th ACM Recommender Systems Conference (RecSys’23), Singapore, Singapore. Zenodo. https://doi.org/10.5281/zenodo.8372449
    Download: PDF (486 KB)
    Project: HumRec (2021–2025)
  • Exploring Effect-Size-Based Meta-Analysis for Multi-Dataset Evaluation / Sertkan, M., Althammer, S., Hofstätter, S., Knees, P., & Neidhardt, J. (2023). Exploring Effect-Size-Based Meta-Analysis for Multi-Dataset Evaluation. In Proceedings of the 3rd Workshop Perspectives on the Evaluation of Recommender Systems 2023 co-located with the 17th ACM Conference on Recommender Systems (RecSys 2023). PERSPECTIVES 2023 - Perspectives on the Evaluation of Recommender Systems Workshop co-located with the 17th ACM Conference on Recommender Systems, Singapore, Singapore. CEUR-WS.org. https://doi.org/10.34726/5352
    Download: PDF (461 KB)
    Project: CDL-RecSys (2022–2028)
  • MuRS: Music Recommender Systems Workshop / Ferraro, A., Knees, P., Quadrana, M., Ye, T., & Gouyon, F. (2023). MuRS: Music Recommender Systems Workshop. In J. Zhang, L. Chen, S. Berkovsky, J.-M. Zhang, T. Di Noia, J. Basilico, L. Pizzato, & Y. Song (Eds.), Proceedings of the Seventeenth ACM Conference on Recommender Systems, Singapore, 18th–22nd September 2023 (pp. 1227–1230). Association for Computing Machinery (ACM). https://doi.org/10.1145/3604915.3608750
    Download: PDF (421 KB)
    Project: HumRec (2021–2025)
  • Connecting Ethnomusicology Data Collections Using Distributed Repositories and Linked Data Technology / Weise, M., Knees, P., Hofmann, A., Ahmedaja, A., Anda Beitāne, & Rauber, A. (2023, May 24). Connecting Ethnomusicology Data Collections Using Distributed Repositories and Linked Data Technology [Conference Presentation]. 3rd Conference of the Portuguese ICTM National Committee, Aveiro, Portugal. https://doi.org/10.34726/4323
    Download: PDF (2.18 MB)
  • MILC 2023: 3rd Workshop on Intelligent Music Interfaces for Listening and Creation / Knees, P., & Lerch, A. (2023). MILC 2023: 3rd Workshop on Intelligent Music Interfaces for Listening and Creation. In Companion Proceedings of 2023 28th Annual Conference on Intelligent User Interfaces (IUI 2023 Companion) (pp. 185–186). Association for Computing Machinery. https://doi.org/10.1145/3581754.3584164
    Download: PDF (409 KB)
    Project: HumRec (2021–2025)
  • Digital Humanism: The Time Is Now / Werthner, H., Stanger, A., Schiaffonati, V., Knees, P., Hardman, L., & Ghezzi, C. (2023). Digital Humanism: The Time Is Now. Computer, 56(1), 138–142. https://doi.org/10.1109/MC.2022.3219528
    Project: DigHum Roadmap (2021–2023)
  • Recommender Systems: Techniques, Effects, and Measures Toward Pluralism and Fairness / Knees, P., Neidhardt, J., & Nalis-Neuner, I. (2023). Recommender Systems: Techniques, Effects, and Measures Toward Pluralism and Fairness. In H. Werthner, C. Ghezzi, & J. Kramer (Eds.), Introduction to Digital Humanism : A Textbook (pp. 417–434). Springer. https://doi.org/10.1007/978-3-031-45304-5_27
    Projects: CDL-RecSys (2022–2028) / HumRec (2021–2025)
  • Listener awareness in music recommender systems: directions and current trends / Knees, P., Schedl, M., Ferwerda, B., & Laplante, A. (2023). Listener awareness in music recommender systems: directions and current trends. In M. Augstein, E. Herder, & W. Wörndl (Eds.), Personalized Human-Computer Interaction (pp. 279–312). DeGruyter Oldenbourg. https://doi.org/10.1515/9783110988567-011
    Project: HumRec (2021–2025)
  • Causality Prediction with Neural-Symbolic Systems: A Case Study in Smart Grids / Schreiberhuber, K., Sabou, M., Ekaputra, F. J., Knees, P., Aryan, P. R., Einfalt, A., & Mosshammer, R. (2023). Causality Prediction with Neural-Symbolic Systems: A Case Study in Smart Grids. In Proceedings of the 17th International Workshop on Neural-Symbolic Learning and Reasoning (NeSy 2023) (pp. 336–347). CEUR-WS.org. https://doi.org/10.34726/5300
    Download: PDF (2.48 MB)
    Project: HumRec (2021–2025)

2022

  • On the Impact and Interplay of Input Representations and Network Architectures for Automatic Music Tagging / Damböck, M., Vogl, R., & Knees, P. (2022). On the Impact and Interplay of Input Representations and Network Architectures for Automatic Music Tagging. In P. Rao, H. Murphy, A. Srinivasamurthy, R. Bittner, R. Caro Repetto, M. Goto, X. Serra, & M. Miron (Eds.), Proceedings of the 23rd International Society for Music Information Retrieval Conference. ISMIR 2022 (pp. 941–948). International Society for Music Information Retrieval. https://doi.org/10.5281/zenodo.7343091
    Download: PDF (655 KB)
    Project: HumRec (2021–2025)
  • A Reproducibility Study on User-centric MIR Research and Why it is Important / Knees, P., Ferwerda, B., Rauber, A., Strumbelj, S., Resch, A., Tomandl, L., Bauer, V., Tang, F. Y., Bobinac, J., Ceranic, A., & Dizdar, R. (2022). A Reproducibility Study on User-centric MIR Research and Why it is Important. In P. Rao, H. Murthy, A. Srinivasamurthy, R. Bittner, R. Caro Repetto, M. Goto, X. Serra, & M. Miron (Eds.), Proceedings of the 23rd International Society for Music Information Retrieval (ISMIR) Conference (pp. 764–771). International Society for Music Information Retrieval. https://doi.org/10.5281/zenodo.7316775
    Download: Camera-ready version (150 KB)
    Project: HumRec (2021–2025)
  • Bias and Feedback Loops in Music Recommendation: Studies on Record Label Impact / Knees, P., Ferraro, A., & Hübler, M. (2022). Bias and Feedback Loops in Music Recommendation: Studies on Record Label Impact. In H. Abdollahpouri, S. Sahebi, M. Elahi, M. Mansoury, B. Loni, Z. Nazari, & M. Dimakopoulou (Eds.), MORS 2022. Proceedings of the 2nd Workshop on Multi-Objective Recommender Systems, co-located with 16th ACM Conference on Recommender Systems (RecSys 2022. CEUR-WS.org. https://doi.org/10.34726/3723
    Download: Camera-ready version (1.13 MB)
    Project: HumRec (2021–2025)
  • Music Information Retrieval and Recommendation: Recent and Future Developments / Knees, P. (2022, August 22). Music Information Retrieval and Recommendation: Recent and Future Developments [Presentation]. Georgia Tech Center for Music Technology Seminar Series, Atlanta, GA, United States of America (the).
  • ReStyle-MusicVAE: Enhancing User Control of Deep Generative Music Models with Expert Labeled Anchors / Prvulovic, D., Vogl, R., & Knees, P. (2022). ReStyle-MusicVAE: Enhancing User Control of Deep Generative Music Models with Expert Labeled Anchors. In A. Bellogin, L. Boratto, O. C. Santos, L. Ardissono, & B. Knijnenburg (Eds.), Adjunct Proceedings of the 30th ACM Conference on User Modeling, Adaptation and Personalization (pp. 63–66). Association for Computing Machinery. https://doi.org/10.1145/3511047.3536412
    Project: HumRec (2021–2025)
  • Scaling Up Broken Systems? Considerations from the Area of Music Streaming / Knees, P. (2022). Scaling Up Broken Systems? Considerations from the Area of Music Streaming. In H. Werthner, E. Prem, E. A. Lee, & C. Ghezzi (Eds.), Perspectives on Digital Humanism (pp. 165–171). Springer Nature Switzerland AG. https://doi.org/10.1007/978-3-030-86144-5_23

2021

2020

2019

  • Predicting user demographics from music listening information / Krismayer, T., Schedl, M., Knees, P., & Rabiser, R. (2019). Predicting user demographics from music listening information. Multimedia Tools and Applications, 78(3), 2897–2920. https://doi.org/10.1007/s11042-018-5980-y
    Project: SmarterJam (2017–2019)
  • 9. User awareness in music recommender systems / Knees, P., Schedl, M., Ferwerda, B., & Laplante, A. (2019). 9. User awareness in music recommender systems. In M. Augstein, E. Herder, & W. Wörndl (Eds.), Personalized Human-Computer Interaction (pp. 223–252). DeGruyter. https://doi.org/10.1515/9783110552485-009
  • Intelligent music interfaces for listening and creation / Knees, P., Schedl, M., & Fiebrink, R. (2019). Intelligent music interfaces for listening and creation. In Proceedings of the 24th International Conference on Intelligent User Interfaces: Companion. 24th International Conference on Intelligent User Interfaces, Marina del Rey, CA, United States of America (the). ACM. https://doi.org/10.1145/3308557.3313110
  • Preface to the 2nd Workshop on Intelligent Music Interfaces for Listening and Creation (MILC) / Knees, P., Schedl, M., & Fiebrink, R. (2019). Preface to the 2nd Workshop on Intelligent Music Interfaces for Listening and Creation (MILC). In C. Trattner, D. Parra, & N. Riche (Eds.), Joint Proceedings of the ACM IUI 2019 Workshops (p. 2). CEUR-WS.org. http://hdl.handle.net/20.500.12708/58093
  • Intelligent User Interfaces for Music Discovery: The Past 20 Years and What's to Come / Knees, P., Schedl, M., & Goto, M. (2019). Intelligent User Interfaces for Music Discovery: The Past 20 Years and What’s to Come. In A. Flexer, G. Peeters, J. Urbano, & A. Volk (Eds.), Proceedings of the 20th International Society for Music Information Retrieval Conference (pp. 44–53). Zenodo. https://doi.org/10.5281/zenodo.3527737
  • Ethnomusicology meets Music Information Retrieval: Towards Automated Segmentation and Analysis / Knees, P. (2019). Ethnomusicology meets Music Information Retrieval: Towards Automated Segmentation and Analysis. 2nd Workshop on Distributed Ethnomusicology data and MIR in the framework of ASEA-UNINET, Mahidol University, Thailand. http://hdl.handle.net/20.500.12708/87006
  • Introduction to Music Information Retrieval / Knees, P. (2019). Introduction to Music Information Retrieval. 1st Workshop on Distributed Ethnomusicology Data and Music Information Retrieval, Mahidol University, Thailand. http://hdl.handle.net/20.500.12708/87003
  • Towards an alliance for distributed music data? / Sağlam, H., Hofmann, A., Ahmedaja, A., Miksa, T., & Knees, P. (2019). Towards an alliance for distributed music data? 45th International Council for Traditional Music World Conference, Bangkok, Thailand. http://hdl.handle.net/20.500.12708/87007
  • An automatic drum machine with touch UI based on a generative neural network / Vogl, R., Eghbal-Zadeh, H., & Knees, P. (2019). An automatic drum machine with touch UI based on a generative neural network. In Proceedings of the 24th International Conference on Intelligent User Interfaces: Companion. 24th International Conference on Intelligent User Interfaces, Marina del Rey, CA, United States of America (the). ACM. https://doi.org/10.1145/3308557.3308673
  • Recommenders and Intelligent Tools in Music Creation: Why, Why Not, and How? / Bauer, C., Knees, P., Vogl, R., & Raber, H. (2019). Recommenders and Intelligent Tools in Music Creation: Why, Why Not, and How? Ars Electronica 2019 - AIxMusic Workshops, Linz, Austria. http://hdl.handle.net/20.500.12708/87008
  • Music Information Retrieval: Inside and Outside the Music / Bauer, C., & Knees, P. (2019). Music Information Retrieval: Inside and Outside the Music. Ars Electronica 2019 - AIxMusic Matinée, Linz, Austria. http://hdl.handle.net/20.500.12708/87009
  • From a Critical Take on Music Recommendation to Digital Humanism / Knees, P. (2019). From a Critical Take on Music Recommendation to Digital Humanism. Critical Perspectives on Data Science and Machine Learning, KTH Stockholm, Sweden. http://hdl.handle.net/20.500.12708/87012
  • On Stakeholders and Data Biases in Music Recommendation / Knees, P. (2019). On Stakeholders and Data Biases in Music Recommendation. Media Technology and Interaction Design Seminar, KTH Stockholm, Sweden. http://hdl.handle.net/20.500.12708/87011
  • New Intelligent Tools in Music Creation? A Case for User-Centric Research / Knees, P. (2019). New Intelligent Tools in Music Creation? A Case for User-Centric Research. User Experience Design, Jönköping University, Sweden. http://hdl.handle.net/20.500.12708/87010
  • Die perfekte Musikempfehlung - Perfekt für wen? / Knees, P. (2019). Die perfekte Musikempfehlung - Perfekt für wen? Future Music Camp 2019, Mannheim, Germany. http://hdl.handle.net/20.500.12708/87005
  • Multi-Task Learning of Tempo and Beat: Learning One to Improve the Other / Böck, S., Davies, M., & Knees, P. (2019). Multi-Task Learning of Tempo and Beat: Learning One to Improve the Other. In A. Flexer, G. Peeters, J. Urbano, & A. Volk (Eds.), Proceedings of the 20th International Society for Music Information Retrieval Conference (pp. 486–493). Zenodo. https://doi.org/10.5281/zenodo.3527849
  • A Proposal for a Neutral Music Recommender System / Knees, P. (2019). A Proposal for a Neutral Music Recommender System. In M. Miron (Ed.), Proceedings of the 1st Workshop on Designing Human-Centric Music Information Research Systems (pp. 4–7). http://hdl.handle.net/20.500.12708/58098
  • Towards Uncovering Dataset Biases: Investigating Record Label Diversity in Music Playlists / Knees, P., & Hübler, M. (2019). Towards Uncovering Dataset Biases: Investigating Record Label Diversity in Music Playlists. In M. Miron (Ed.), Proceedings of the 1st Workshop on Designing Human-Centric Music Information Research Systems (pp. 19–22). http://hdl.handle.net/20.500.12708/58097
  • RecSys challenge 2019 / Knees, P., Deldjoo, Y., Moghaddam, F. B., Adamczak, J., Leyson, G.-P., & Monreal, P. (2019). RecSys challenge 2019. In Proceedings of the 13th ACM Conference on Recommender Systems. 13th ACM Conference on Recommender Systems, Copenhagen, Denmark. ACM. https://doi.org/10.1145/3298689.3346974
  • Multi-Task Music Representation Learning from Multi-Label Embeddings / Schindler, A., & Knees, P. (2019). Multi-Task Music Representation Learning from Multi-Label Embeddings. In 2019 International Conference on Content-Based Multimedia Indexing (CBMI). 2019 International Conference on Content-Based Multimedia Indexing (CBMI), Dublin, Ireland. IEEE. https://doi.org/10.1109/cbmi.2019.8877462
  • Artificial Intelligence and the Future of Business / Knees, P., Wasner, C., & Krenn, B. (2019). Artificial Intelligence and the Future of Business. Vienna International Business Club, Expat Center of the Vienna Business Agency, Austria. http://hdl.handle.net/20.500.12708/87004
  • Towards Recommender Systems for Music Creators / Knees, P. (2019). Towards Recommender Systems for Music Creators. HCI Lunchtime Scientific Series, TU Wien, Austria. http://hdl.handle.net/20.500.12708/87002
  • Introduction to Music Information Retrieval / Knees, P. (2019). Introduction to Music Information Retrieval. Informatik BEGINNER’s Day 2019, TU Wien, Austria. http://hdl.handle.net/20.500.12708/87001
  • RecSys Challenge '19: Proceedings of the Workshop on ACM Recommender Systems Challenge / Knees, P., Deldjoo, Y., Bakhshandegan Moghaddam, F., Adamczak, J., Leyson, G.-P., & Monreal, P. (Eds.). (2019). RecSys Challenge ’19: Proceedings of the Workshop on ACM Recommender Systems Challenge. ACM. https://doi.org/10.1145/3359555

2018

2017

2004