2025W

• Bachelor Thesis for Informatics and Business Informatics / 188.944 / PR
• Digital Humanism / 194.072 / VU
• Experiment Design and Execution / 194.192 / VU
• Interdisciplinary Project in Data Science / 194.147 / PR
• Introduction to Information Retrieval / 194.166 / VU
• Project in Computer Science 1 / 194.145 / PR
• Reproducible Evaluation and Experimentation in Information Retrieval / 194.208 / VU
• Research seminar for PhD students / 188.081 / SE
• Seminar for Master Students in Data Science / 180.772 / SE

2026S

• Advanced Information Retrieval / 188.980 / VU
• Bachelor Thesis for Informatics and Business Informatics / 188.944 / PR
• Digital Humanism for Doctoral Students / 194.178 / VU
• Project in Computer Science 1 / 194.145 / PR
• Project in Computer Science 2 / 194.146 / PR
• Research seminar for PhD students / 188.081 / SE
• Seminar for Master Students in Data Science / 180.772 / SE

• Vienna Doctoral College on Digital Humanism
  2024 – 2029 / Vienna Science and Technology Fund (WWTF)
  Publications: 223057 / 223058 / 227936
• European Inclusive Education for Digital Society, Social Innovation and Global Citizenship
  2024 – 2027 / European Commission
• Fostering Austria's Innovative Strength and Research excellence in Artificial Intelligence
  2024 – 2026 / Austrian Research Promotion Agency (FFG)
  Publications: 209781 / 210632 / 216865 / 224468 / 224501
• Humans and Recommender Systems: Towards Mutual Understanding
  2021 – 2025 / Austrian Science Fund (FWF)
  Publications: 150306 / 161680 / 177462 / 177655 / 193567 / 190669 / 191188 / 211127 / 191171 / 193576 / 193569 / 209781 / 209942 / 210045 / 209944 / 209318 / 212628
• Digital Humanism Roadmap: Chancen an der Schnittstelle von Innovation und Forschung
  2021 – 2023 / Vienna Business Agency (WAW)
  Publications: 139746 / 175643
• DigHumHub
  2020 – 2021 / MA 7 der Stadt Wien
• Automatic recommendation of music tracks and musicians for collaboration in an online jam community
  2017 – 2019 / Austrian Research Promotion Agency (FFG)
  Publications: 142406 / 56721 / 57031 / 57033 / 57035 / 57038 / 57042 / 57190 / 57191 / 57451 / 57452 / 57453 / 57618 / 57622 / 57649 / 86636 / 86810

2025

• Understanding Verbatim Memorization in LLMs Through Circuit Discovery / Lasy, I., Knees, P., & Woltran, S. (2025). Understanding Verbatim Memorization in LLMs Through Circuit Discovery. arXiv. https://doi.org/10.48550/ARXIV.2506.21588
• The Need for Tailored Support for Long-Term Students in Informatics Bachelor’s Programmes / König, F. L., Kern, B. M. J., & Knees, P. (2025). The Need for Tailored Support for Long-Term Students in Informatics Bachelor’s Programmes. In A. Curaj, C. M. Hâj, & R. Pricopie (Eds.), European Higher Education Area 2030: Bridging Realities for Tomorrow’s Higher Education (pp. 285–293). Springer. https://doi.org/10.1007/978-3-031-75140-0_17
  Download: Full paper (236 KB)
• Towards Playlist Continuation Through Large-Scale Context and Audio-Based Music Representations / Shashaani, S., & Knees, P. (2025). Towards Playlist Continuation Through Large-Scale Context and Audio-Based Music Representations. In A. Ferraro, L. Porcaro, & C. Bauer (Eds.), Proceedings of the 3rd Music Recommender Systems Workshop (MuRS 2025) co-located with the 19th ACM Conference on Recommender Systems (RecSys 2025). CEUR-WS. http://hdl.handle.net/20.500.12708/223057
  Project: doc.hum (2024–2029)
• An Analysis of the Evolution of Music Listening Data and the Need for Task Discernment / Shashaani, S., Seshadri, P., & Knees, P. (2025). An Analysis of the Evolution of Music Listening Data and the Need for Task Discernment. In A. Ferraro, L. Porcaro, & C. Bauer (Eds.), Proceedings of the 3rd Music Recommender Systems Workshop (MuRS 2025) co-located with the 19th ACM Conference on Recommender Systems (RecSys 2025). CEUR-WS.
  Project: doc.hum (2024–2029)
• Understanding Verbatim Memorization in LLMs Through Circuit Discovery / Lasy, I., Knees, P., & Woltran, S. (2025). Understanding Verbatim Memorization in LLMs Through Circuit Discovery. In R. Jia, E. Wallace, Y. Huang, T. Pimentel, P. Maini, V. Dankers, J. T.-Z. Wei, & P. Lesci (Eds.), Proceedings of the First Workshop on Large Language Model Memorization (L2M2) (pp. 83–94). Association for Computational Linguistics. https://doi.org/10.18653/v1/2025.l2m2-1.7
  Project: BILAI (2024–2029)

2024

• MuRS 2024: 2nd Music Recommender Systems Workshop / Ferraro, A., Porcaro, L., Knees, P., & Bauer, C. (2024). MuRS 2024: 2nd Music Recommender Systems Workshop. In T. Di Noia, P. Lops, T. Joachims, K. Verbert, P. Castells, Z. Dong, & B. London (Eds.), RecSys ’24: Proceedings of the 18th ACM Conference on Recommender Systems (pp. 1202–1205). Association for Computing Machinery. https://doi.org/10.1145/3640457.3687097
  Project: HumRec (2021–2025)
• Mission Reproducibility: An Investigation on Reproducibility Issues in Machine Learning and Information Retrieval Research / Staudinger, M., Kern, B. M. J., Miksa, T., Arnhold, L., Knees, P., Rauber, A., & Hanbury, A. (2024). Mission Reproducibility: An Investigation on Reproducibility Issues in Machine Learning and Information Retrieval Research. In Proceedings 2024 IEEE 20th International Conference on e-Science (e-Science). IEEE eScience 2024, Osaka, Japan. IEEE. https://doi.org/10.1109/e-Science62913.2024.10678657
  Projects: FAIR-AI (2024–2026) / HumRec (2021–2025)
• Content-driven music recommendation: Evolution, state of the art, and challenges / Deldjoo, Y., Schedl, M., & Knees, P. (2024). Content-driven music recommendation: Evolution, state of the art, and challenges. Computer Science Review, 51, Article 100618. https://doi.org/10.1016/j.cosrev.2024.100618
  Project: HumRec (2021–2025)
• Enhancing Sequential Music Recommendation with Negative Feedback-informed Contrastive Learning / Seshadri, P., Shashaani, S., & Knees, P. (2024). Enhancing Sequential Music Recommendation with Negative Feedback-informed Contrastive Learning. In T. Di Noia, P. Lops, T. Joachims, K. Verbert, P. Castells, Z. Dong, & B. London (Eds.), RecSys ’24: Proceedings of the 18th ACM Conference on Recommender Systems (pp. 1028–1032). Association for Computing Machinery. https://doi.org/10.1145/3640457.3688188
  Download: PDF (575 KB)
  Project: HumRec (2021–2025)
• Mosaikbox: Improving Fully Automatic DJ Mixing Through Rule-based Stem Modification And Precise Beat-Grid Estimation / Sowula, R., & Knees, P. (2024). Mosaikbox: Improving Fully Automatic DJ Mixing Through Rule-based Stem Modification And Precise Beat-Grid Estimation. In B. Kaneshiro, G. Mysore, O. Nieto, C. Donahue, C.-Z. A. Huang, J. H. Lee, B. McFee, & M. C. McCallum (Eds.), Proceedings of the 25th International Society for Music Information Retrieval Conference (pp. 850–857). International Society for Music Information Retrieva. https://doi.org/10.5281/zenodo.14877463
  Download: Camera Ready Paper (272 KB)
  Project: HumRec (2021–2025)
• Digitalisierung und wir: Lehrbuch zum Digitalen Humanismus mit praktischen Übungen / Eichinger, A., Knees, P., & Werthner, H. (Eds.). (2024). Digitalisierung und wir: Lehrbuch zum Digitalen Humanismus mit praktischen Übungen. Residenz. https://doi.org/10.34726/8506
  Download: Full Textbook (2.73 MB)
  Project: FAIR-AI (2024–2026)

2023

• 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)
• 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)
• 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)
• 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)
• 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)

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

• An Overview of Music Retrieval and Recommendation: From Describing Sound to Asking What is Fair / Knees, P. (2021). An Overview of Music Retrieval and Recommendation: From Describing Sound to Asking What is Fair. In M. Tkalcic, V. Pejović, M. Kljun, & K. Čopič Pucihar (Eds.), Proceedings of the 6th Human-Computer Interaction Slovenia Conference (p. 2). CEUR-WS. http://hdl.handle.net/20.500.12708/58756
• Panel on: Human-centered AI - Are we there yet? / Knees, P., Bauer, C., Lex, E., Sacharidis, D., & Tkalcic, M. (2021). Panel on: Human-centered AI - Are we there yet? 5th HUMANIZE Workshop on Transparency and Explainability in Adaptive Systems through User Modeling Grounded in Psychological Theory, College Station, TX, USA, United States of America (the). http://hdl.handle.net/20.500.12708/87299
• Enabling FAIR use of Ethnomusicology Data - Through Distributed Repositories, Linked Data and Music Information Retrieval / Hofmann, A., Miksa, T., Knees, P., Bakos, A., Sağlam, H., Ahmedaja, A., Yimwadsana, B., Chan, C., & Rauber, A. (2021). Enabling FAIR use of Ethnomusicology Data - Through Distributed Repositories, Linked Data and Music Information Retrieval. Empirical Musicology Review, 16(1), 47–64. https://doi.org/10.18061/emr.v16i1.7632
• Datengestützte Empfehlungssysteme – Kuratiertes Musikangebot. / Knees, P. (2021). Datengestützte Empfehlungssysteme – Kuratiertes Musikangebot. In H. Wandjo & A. Endreß (Eds.), Musikwirtschaft im Zeitalter der Digitalisierung (pp. 419–432). Nomos Verlagsgesellschaft mbH & Co. KG. https://doi.org/10.5771/9783845276939-419
• Machine Learning Applied to Music/Audio Signal Processing / Lerch, A., & Knees, P. (2021). Machine Learning Applied to Music/Audio Signal Processing. Electronics, 10(24), 3077. https://doi.org/10.3390/electronics10243077
• Nachvollziehbare KI / Knees, P. (2021). Nachvollziehbare KI. Digitale Kompetenzen @ Parlament, Wien, Austria. http://hdl.handle.net/20.500.12708/87300
• Special Issue "Machine Learning Applied to Music/Audio Signal Processing" / Lerch, A., & Knees, P. (Eds.). (2021). Special Issue “Machine Learning Applied to Music/Audio Signal Processing.” MDPI. http://hdl.handle.net/20.500.12708/24931

2020

• Session-based Hotel Recommendations Dataset: As part of the ACM Recommender System Challenge 2019 / Adamczak, J., Deldjoo, Y., Moghaddam, F. B., Knees, P., Leyson, G.-P., & Monreal, P. (2020). Session-based Hotel Recommendations Dataset: As part of the ACM Recommender System Challenge 2019. ACM Transactions on Intelligent Systems and Technology, 12(1), 1–20. https://doi.org/10.1145/3412379
• Information retrieval and recommender systems for music listening and creation / Knees, P. (2020). Information retrieval and recommender systems for music listening and creation [Professorial Dissertation, Technische Universität Wien]. reposiTUm. http://hdl.handle.net/20.500.12708/158884
• Intelligent User Interfaces for Music Discovery / Knees, P., Schedl, M., & Goto, M. (2020). Intelligent User Interfaces for Music Discovery. Transactions of the International Society for Music Information Retrieval, 3(1), 165–179. https://doi.org/10.5334/tismir.60
• Special Issue on User Modeling for Personalized Interaction with Music / Tkalcic, M., Schedl, M., & Knees, P. (Eds.). (2020). Special Issue on User Modeling for Personalized Interaction with Music. Springer Nature Switzerland AG. http://hdl.handle.net/20.500.12708/24932
• Preface to the Special Issue on User Modeling for Personalized Interaction with Music / Tkalčič, M., Schedl, M., & Knees, P. (2020). Preface to the Special Issue on User Modeling for Personalized Interaction with Music. User Modeling and User-Adapted Interaction, 30(2), 195–198. https://doi.org/10.1007/s11257-020-09264-6
• Music Tower Blocks: Multi-Faceted Exploration Interface for Web-Scale Music Access / Schedl, M., Mayr, M., & Knees, P. (2020). Music Tower Blocks: Multi-Faceted Exploration Interface for Web-Scale Music Access. In Proceedings of the 2020 International Conference on Multimedia Retrieval. 2020 International Conference on Multimedia Retrieval (ICMR ´20), Dublin, Ireland. ACM. https://doi.org/10.1145/3372278.3391928
• Neutrality and Fairness in Music Recommendation: A Matter of Digital Humanism / Knees, P. (2020). Neutrality and Fairness in Music Recommendation: A Matter of Digital Humanism. Georgia Tech Center for Music Technology Seminar Series, Atlanta, United States of America (the). http://hdl.handle.net/20.500.12708/87097
• Predicting MPI Collective Communication Performance Using Machine Learning / Hunold, S., Bhatele, A., Bosilca, G., & Knees, P. (2020). Predicting MPI Collective Communication Performance Using Machine Learning. In 2020 IEEE International Conference on Cluster Computing (CLUSTER). IEEE International Conference on Cluster Computing (IEEE Cluster 2020) - Online Conference, Kobe, Japan. IEEE. https://doi.org/10.1109/cluster49012.2020.00036
• Unsupervised cross-modal audio representation learning from unstructured multilingual text / Schindler, A., Gordea, S., & Knees, P. (2020). Unsupervised cross-modal audio representation learning from unstructured multilingual text. In Proceedings of the 35th Annual ACM Symposium on Applied Computing. 35th Annual ACM Symposium on Applied Computing (SAC ´20), Brno, Czechia. ACM. https://doi.org/10.1145/3341105.3374114
• Music Information Retrieval for Building Intelligent Music Creation Tools / Knees, P. (2020). Music Information Retrieval for Building Intelligent Music Creation Tools. Interactive Music Technologies, St. Gilgen, Austria. http://hdl.handle.net/20.500.12708/87056
• The ACM Multimedia 2020 Interactive Arts Exhibition: Human and AI Generated Multimedia / Knees, P., & Gan, Z. (Eds.). (2020). The ACM Multimedia 2020 Interactive Arts Exhibition: Human and AI Generated Multimedia. GitHub. http://hdl.handle.net/20.500.12708/24762

2019

• RecSys challenge 2019: session-based hotel recommendations / Knees, P., Deldjoo, Y., Moghaddam, F. B., Adamczak, J., Leyson, G.-P., & Monreal, P. (2019). RecSys challenge 2019: session-based hotel recommendations. 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
• 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
• 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
• 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
• 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

• MIREX Submission for Drum Transcription 2018 / Vogl, R., & Knees, P. (2018). MIREX Submission for Drum Transcription 2018. In 14th Music Information Retrieval Evaluation eXchange (MIREX 2018) (p. 2). International Music Information Retrieval Systems Evaluation Laboratory, School of Information Sciences, University of Illinois at Urbana-Champaign. http://hdl.handle.net/20.500.12708/56721
  Project: SmarterJam (2017–2019)
• IUI'18 Workshop on Intelligent Music Interfaces for Listening and Creation (MILC) / Knees, P., Schedl, M., & Fiebrink, R. (2018). IUI’18 Workshop on Intelligent Music Interfaces for Listening and Creation (MILC). In A. Said & T. Komatsu (Eds.), Joint Proceedings of the ACM IUI 2018 Workshops (p. 2). CEUR-WS.org. http://hdl.handle.net/20.500.12708/57621
• Music Genre Classification Revisited: An In-Depth Examination Guided by Music Experts / Pálmason, H., Jónsson, B. Þ., Schedl, M., & Knees, P. (2018). Music Genre Classification Revisited: An In-Depth Examination Guided by Music Experts. In M. Aramaki, M. Davies, R. Kronland-Martinet, & S. Ystad (Eds.), Music Technology with Swing (pp. 49–62). Springer. https://doi.org/10.1007/978-3-030-01692-0_4
  Project: SmarterJam (2017–2019)
• Towards Visual Interfaces to Sound and Music Retrieval / Knees, P. (2018). Towards Visual Interfaces to Sound and Music Retrieval. National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba, Japan. http://hdl.handle.net/20.500.12708/86811
• Towards Multi-Instrument Drum Transcription / Vogl, R., Widmer, G., & Knees, P. (2018). Towards Multi-Instrument Drum Transcription. In Proceedings of the 21st International Conference on Digital Audio Effects (DAFx-18) (pp. 57–64). http://hdl.handle.net/20.500.12708/57451
  Project: SmarterJam (2017–2019)
• A GAN based Drum Pattern Generation UI Prototype / Eghbal-Zadeh, H., Vogl, R., Widmer, G., & Knees, P. (2018). A GAN based Drum Pattern Generation UI Prototype. In 19th International Society for Music Information Retrieval Conference: Late-Breaking Demos Session (p. 2). http://hdl.handle.net/20.500.12708/57452
  Project: SmarterJam (2017–2019)
• GANs and Poses: An Interactive Generative Music Installation Controlled by Dance Moves / Vogl, R., Eghbal-Zadeh, H., Widmer, G., & Knees, P. (2018). GANs and Poses: An Interactive Generative Music Installation Controlled by Dance Moves. In 19th International Society for Music Information Retrieval Conference: Interactive Machine-Learning for Music @Exhibition (p. 5). http://hdl.handle.net/20.500.12708/57453
  Project: SmarterJam (2017–2019)
• Overview and New Challenges of Music Recommendation Research in 2018 / Schedl, M., Knees, P., & Gouyon, F. (2018). Overview and New Challenges of Music Recommendation Research in 2018. 19th International Society for Music Information Retrieval Conference, Paris, France. http://hdl.handle.net/20.500.12708/86810
  Project: SmarterJam (2017–2019)
• Künstliche Intelligenz als personalisierter Komponist - Automatische Musikerzeugung als das Ende der Tantiemen? / Knees, P. (2018). Künstliche Intelligenz als personalisierter Komponist - Automatische Musikerzeugung als das Ende der Tantiemen? Future Music Camp 2018, Mannheim, Germany. http://hdl.handle.net/20.500.12708/86812
• Multimedia recommender systems / Deldjoo, Y., Schedl, M., Hidasi, B., & Knees, P. (2018). Multimedia recommender systems. In Proceedings of the 12th ACM Conference on Recommender Systems. ACM, Austria. ACM. https://doi.org/10.1145/3240323.3241620
  Project: SmarterJam (2017–2019)
• Selected Topics in Music Recommendation / Knees, P. (2018). Selected Topics in Music Recommendation. E-Commerce Research Seminar for Ph.D. Students 2018W, TU Wien, Austria. http://hdl.handle.net/20.500.12708/86815
• Music and Sound Recommendation for Listeners and Creators / Knees, P. (2018). Music and Sound Recommendation for Listeners and Creators. eBusiness in the Creative Industries, University of Vienna, Austria. http://hdl.handle.net/20.500.12708/86813

2017

• UMAP 2017 Workshop on Surprise, Opposition, and Obstruction in Adaptive and Personalized Systems : Organizers' Welcome & Organization / Knees, P., Andersen, K., Said, A., & Tkalcic, M. (2017). UMAP 2017 Workshop on Surprise, Opposition, and Obstruction in Adaptive and Personalized Systems : Organizers’ Welcome & Organization. In Adjunct Publication of the 25th Conference on User Modeling, Adaptation and Personalization. UMAP’17 - 25th Conference on User Modeling, Adaptation and Personalization, Bratislava, Slovakia. ACM. https://doi.org/10.1145/3099023.3099095
• MIREX Submission for Drum Transcription 2017 / Vogl, R., Dorfer, M., Widmer, G., & Knees, P. (2017). MIREX Submission for Drum Transcription 2017. In 13th Music Information Retrieval Evaluation eXchange (MIREX 2017) (p. 1). International Music Information Retrieval Systems Evaluation Laboratory, School of Information Sciences, University of Illinois at Urbana-Champaign. http://hdl.handle.net/20.500.12708/57649
  Project: SmarterJam (2017–2019)
• Indicators of Country Similarity in Terms of Music Taste, Cultural, and Socio-economic Factors / Schedl, M., Lemmerich, F., Ferwerda, B., Skowron, M., & Knees, P. (2017). Indicators of Country Similarity in Terms of Music Taste, Cultural, and Socio-economic Factors. In 2017 IEEE International Symposium on Multimedia (ISM). 2017 IEEE International Symposium on Multimedia, Taichung, Taiwan (Province of China). IEEE. https://doi.org/10.1109/ism.2017.55
• Drum transcription from polyphonic music with recurrent neural networks / Vogl, R., Dorfer, M., & Knees, P. (2017). Drum transcription from polyphonic music with recurrent neural networks. In 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), New Orleans, LA, United States of America (the). https://doi.org/10.1109/icassp.2017.7952146
  Project: SmarterJam (2017–2019)
• An Intelligent Drum Machine for Electronic Dance Music Production and Performance / Vogl, R., & Knees, P. (2017). An Intelligent Drum Machine for Electronic Dance Music Production and Performance. In Proceedings of the 17th International Conference on New Interfaces for Musical Expression (pp. 231–236). http://hdl.handle.net/20.500.12708/57033
  Project: SmarterJam (2017–2019)
• Drum Transcription via Joint Beat and Drum Modeling Using Convolutional Recurrent Neural Networks / Vogl, R., Dorfer, M., Widmer, G., & Knees, P. (2017). Drum Transcription via Joint Beat and Drum Modeling Using Convolutional Recurrent Neural Networks. In Proceedings of the 18th International Society for Music Information Retrieval Conference (pp. 150–157). http://hdl.handle.net/20.500.12708/57031
  Project: SmarterJam (2017–2019)
• Towards Visual Approaches to Audio Retrieval in Creative Music Production / Knees, P. (2017). Towards Visual Approaches to Audio Retrieval in Creative Music Production. Guest Lecture, Bangkok, Thailand. http://hdl.handle.net/20.500.12708/86637
• Introduction to Music Information Retrieval and Music Content Analysis / Knees, P. (2017). Introduction to Music Information Retrieval and Music Content Analysis. Indonesian Summer School on Music Information Retrieval, Depok, Indonesia. http://hdl.handle.net/20.500.12708/86638
• Building Physical Props for Imagining Future Recommender Systems / Knees, P., & Andersen, K. (2017). Building Physical Props for Imagining Future Recommender Systems. In Proceedings of the 2017 ACM Workshop on Theory-Informed User Modeling for Tailoring and Personalizing Interfaces. 2017 ACM Workshop on Theory-Informed User Modeling for Tailoring and Personalizing Interfaces, Limassol, Cyprus. ACM. https://doi.org/10.1145/3039677.3039682
  Project: SmarterJam (2017–2019)
• Prediction of User Demographics from Music Listening Habits / Krismayer, T., Schedl, M., Knees, P., & Rabiser, R. (2017). Prediction of User Demographics from Music Listening Habits. In Proceedings of the 15th International Workshop on Content-Based Multimedia Indexing. 15th International Workshop on Content-Based Multimedia Indexing, Florenz, Italy. ACM. https://doi.org/10.1145/3095713.3095722
• New Paths in Music Recommender Systems Research / Schedl, M., Knees, P., & Gouyon, F. (2017). New Paths in Music Recommender Systems Research. In Proceedings of the Eleventh ACM Conference on Recommender Systems. 11th ACM Conference on Recommender Systems, Como, Italy. ACM. https://doi.org/10.1145/3109859.3109934
  Project: SmarterJam (2017–2019)
• Music Genre Classification Revisited: An In-Depth Examination Guided by Music Experts / Pálmason, H., Jónsson, B. T., Schedl, M., & Knees, P. (2017). Music Genre Classification Revisited: An In-Depth Examination Guided by Music Experts. In R. Kronland-Martinet, S. Ystad, & M. Aramaki (Eds.), Proceedings of the 13th International Symposium on Computer Music Multidisciplinary Research (pp. 45–56). Les éditions de PRISM. http://hdl.handle.net/20.500.12708/57190
  Project: SmarterJam (2017–2019)
• On Competitiveness of Nearest-Neighbor-Based Music Classification: A Methodological Critique / Pálmason, H., Jónsson, B. Þ., Amsaleg, L., Schedl, M., & Knees, P. (2017). On Competitiveness of Nearest-Neighbor-Based Music Classification: A Methodological Critique. In C. Beeck, F. Borutta, P. Kröger, & T. Seidl (Eds.), Similarity Search and Applications (pp. 275–283). Lecture Notes in Computer Science, Springer. https://doi.org/10.1007/978-3-319-68474-1_19
  Project: SmarterJam (2017–2019)
• Music Retrieval and Recommendation: Applications in Music Creation and Collaboration / Knees, P. (2017). Music Retrieval and Recommendation: Applications in Music Creation and Collaboration. 3rd Berlin Music Information Retrieval Meetup, Berlin, Germany. http://hdl.handle.net/20.500.12708/86636
  Project: SmarterJam (2017–2019)
• Music Information Retrieval for Creative Audio Production / Knees, P., & Vogl, R. (2017). Music Information Retrieval for Creative Audio Production. i2c Networking Friday 2017, TU Wien, Austria. http://hdl.handle.net/20.500.12708/86649
• Introduction to Music Information Retrieval / Knees, P. (2017). Introduction to Music Information Retrieval. Informatik BEGINNER’s Day 2017, TU Wien, Austria. http://hdl.handle.net/20.500.12708/86640
• Me, Myself & A.I. / Knees, P. (2017). Me, Myself & A.I. Werbeplanung.at Summit 2017, Vienna, Austria. http://hdl.handle.net/20.500.12708/86639

2004

• Automatische Klassifikation von Musikkünstlern basierend auf Web-Daten / Knees, P. (2004). Automatische Klassifikation von Musikkünstlern basierend auf Web-Daten [Diploma Thesis, Technische Universität Wien]. reposiTUm. https://resolver.obvsg.at/urn:nbn:at:at-ubtuw:1-12836
  Download: PDF (606 KB)

• A Systematic Comparison of Named Entitiy Recognition Approaches for Cyber Threat Intelligence / Ranzinger, M. (2026). A Systematic Comparison of Named Entitiy Recognition Approaches for Cyber Threat Intelligence [Diploma Thesis, Technische Universität Wien]. reposiTUm. https://doi.org/10.34726/hss.2026.131901
  Download: PDF (2.8 MB)
• Evaluation of sparse autoencoder-based refusal features in LLMs : a dataset-dependence study / Kerl, T. (2025). Evaluation of sparse autoencoder-based refusal features in LLMs : a dataset-dependence study [Diploma Thesis, Technische Universität Wien]. reposiTUm. https://doi.org/10.34726/hss.2025.130185
  Download: PDF (1.9 MB)
• Multi-task learning of rhythmic and tonal properties in music / Rjabcsenko, P. (2025). Multi-task learning of rhythmic and tonal properties in music [Diploma Thesis, Technische Universität Wien]. reposiTUm. https://doi.org/10.34726/hss.2025.85241
  Download: PDF (2.38 MB)
• Automatic music mood tagging: EMMA dataset / Kinoshita, Y. (2024). Automatic music mood tagging: EMMA dataset [Diploma Thesis, Technische Universität Wien]. reposiTUm. https://doi.org/10.34726/hss.2024.115181
  Download: PDF (1.5 MB)
• Improving music mixability by using rule-based stem modification and contextual information / Sowula, R. (2024). Improving music mixability by using rule-based stem modification and contextual information [Diploma Thesis, Technische Universität Wien]. reposiTUm. https://doi.org/10.34726/hss.2024.112486
  Download: PDF (4.07 MB)
• A comparative performance analysis of deep reinforcement learning news recommender systems / Veselý, D. (2023). A comparative performance analysis of deep reinforcement learning news recommender systems [Diploma Thesis, Technische Universität Wien]. reposiTUm. https://doi.org/10.34726/hss.2023.95163
  Download: PDF (3.9 MB)
• Context-based tweet engagement prediction / Jeromela, J. (2023). Context-based tweet engagement prediction [Diploma Thesis, Technische Universität Wien]. reposiTUm. https://doi.org/10.34726/hss.2023.79627
  Download: PDF (1.88 MB)
• Comparing neural network architectures for drum pattern generation / Peer, T. (2023). Comparing neural network architectures for drum pattern generation [Diploma Thesis, Technische Universität Wien]. reposiTUm. https://doi.org/10.34726/hss.2023.112485
  Download: PDF (3.16 MB)
• Reproduction of Black-Box music analysis algorithms through machine learning / Schmidt, A. (2023). Reproduction of Black-Box music analysis algorithms through machine learning [Diploma Thesis, Technische Universität Wien]. reposiTUm. https://doi.org/10.34726/hss.2023.86725
  Download: PDF (2.76 MB)
• Audio tampering detection: Deep learning methodologies for multi-layered threat detection / Dörömbözi, A. (2023). Audio tampering detection: Deep learning methodologies for multi-layered threat detection [Diploma Thesis, Technische Universität Wien]. reposiTUm. https://doi.org/10.34726/hss.2023.96575
  Download: PDF (2.81 MB)
• Causality prediction in a cyber-physical-energy-system using knowledge graph embeddings / Schreiberhuber, K. (2023). Causality prediction in a cyber-physical-energy-system using knowledge graph embeddings [Diploma Thesis, Technische Universität Wien]. reposiTUm. https://doi.org/10.34726/hss.2023.102827
  Download: PDF (1.63 MB)
• Analysing music collection datasets to investigate the impact of record labels on music recommender systems / Hübler, M. (2022). Analysing music collection datasets to investigate the impact of record labels on music recommender systems [Diploma Thesis, Technische Universität Wien]. reposiTUm. https://doi.org/10.34726/hss.2022.98121
  Download: PDF (1.74 MB)
• Audio effect modeling with deep learning methods / Bognár, P. (2022). Audio effect modeling with deep learning methods [Diploma Thesis, Technische Universität Wien]. reposiTUm. https://doi.org/10.34726/hss.2022.94860
  Download: PDF (3.54 MB)
• A comparison of audio preprocessing methods for music autotagging using CNN-architectures / Damböck, M. (2022). A comparison of audio preprocessing methods for music autotagging using CNN-architectures [Diploma Thesis, Technische Universität Wien]. reposiTUm. https://doi.org/10.34726/hss.2022.89400
  Download: PDF (3.81 MB)
• Estimating vocal tract resonances of synthesized high-pitched vowels using CNN / Mikušová, I. (2021). Estimating vocal tract resonances of synthesized high-pitched vowels using CNN [Diploma Thesis, Technische Universität Wien]. reposiTUm. https://doi.org/10.34726/hss.2022.89401
  Download: PDF (15 MB)
• Extraction of tabular information from PDF documents : a graph-based approach / Aigner, M. B. (2020). Extraction of tabular information from PDF documents : a graph-based approach [Diploma Thesis, Technische Universität Wien]. reposiTUm. https://doi.org/10.34726/hss.2020.79244
  Download: PDF (2.89 MB)
• Distributed factorization machines for next-track music recommendation / Gabriel, M. (2020). Distributed factorization machines for next-track music recommendation [Diploma Thesis, Technische Universität Wien]. reposiTUm. https://doi.org/10.34726/hss.2020.66381
  Download: PDF (1.36 MB)
• On multilingual natural language processing for social media engagement prediction / Gradinariu, D. (2020). On multilingual natural language processing for social media engagement prediction [Diploma Thesis, Technische Universität Wien]. reposiTUm. http://hdl.handle.net/20.500.12708/78639
• Rule-based recommender for feature engineering in big data / Lepadat, M.-A. (2019). Rule-based recommender for feature engineering in big data [Diploma Thesis, Technische Universität Wien]. reposiTUm. https://doi.org/10.34726/hss.2019.65802
  Download: PDF (1.64 MB)