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 ( reposiTUm)

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 ( reposiTUm)

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 ( reposiTUm)

Shashaani, S. (2024). Explainability in Music Recommender System. 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. 1395–1401). https://doi.org/10.1145/3640457.3688028 ( reposiTUm)

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 ( reposiTUm)

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 ( reposiTUm)

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 ( reposiTUm)

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 ( reposiTUm)

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 ( reposiTUm)

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 ( reposiTUm)

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 ( reposiTUm)

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 ( reposiTUm)

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 ( reposiTUm)

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 ( reposiTUm)

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 ( reposiTUm)

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 ( reposiTUm)

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 ( reposiTUm)