Andreas Peintner, Msc.

@article{peintner:tors:2025,
 address = {New York, NY, USA},
 articleno = {46},
 author = {Peintner, Andreas and Mohammadi, Amir Reza and Zangerle, Eva},
 doi = {10.1145/3701764},
 issue_date = {December 2025},
 journal = {ACM Transactions on Recommender Systems},
 keywords = {Recommender systems, session-based recommendation, graph neural network, supervised contrastive learning},
 month = {April},
 number = {4},
 numpages = {24},
 publisher = {Association for Computing Machinery},
 title = {Efficient Session-based Recommendation with Contrastive Graph-based Shortest Path Search},
 url = {https://doi.org/10.1145/3701764},
 volume = {3},
 year = {2025}
}

@article{peintner:tismir:2025,
 abstract = {Music emotion recognition (MER) seeks to understand the complex emotional landscapes elicited by music, acknowledging music\’s profound social and psychological roles beyond traditional tasks such as genre classification or content similarity. MER relies heavily on high‑quality emotional annotations, which serve as the foundation for training models to recognize emotions. However, collecting these annotations is both complex and costly, leading to limited availability of large‑scale datasets for MER. Recent efforts in MER for automatically extracting emotion have focused on learning track representations in a supervised manner. However, these approaches mainly use simplified emotion models due to limited datasets or a lack of necessity for sophisticated emotion models and ignore hidden inter‑track relations, which are beneficial in a semi‑supervised learning setting. This paper proposes a novel approach to MER by constructing a multi‑relational graph that encapsulates different facets of music. We leverage graph neural networks to model intricate inter‑track relationships and capture structurally induced representations from user data, such as listening histories, genres, and tags. Our model, the semi‑supervised multi‑relational graph neural network for emotion recognition (SRGNN‑Emo), innovates by combining graph‑based modeling with semi‑supervised learning, using rich user data to extract nuanced emotional profiles from music tracks. Through extensive experimentation, SRGNN‑Emo demonstrates significant improvements in R2 and root mean squared error metrics for predicting the intensity of nine continuous emotions (Geneva Emotional Music Scale), demonstrating its superior capability in capturing and predicting complex emotional expressions in music.},
 author = {Peintner, Andreas and Moscati, Marta and Kinoshita, Yu and Vogl, Richard and Knees, Peter and Schedl, Markus and Strauss, Hannah and Zentner, Marcel and Zangerle, Eva},
 doi = {10.5334/tismir.235},
 journal = {Transactions of the International Society for Music Information Retrieval},
 keyword = {en_US},
 month = {Jun},
 title = {Nuanced Music Emotion Recognition via a Semi-Supervised Multi-Relational Graph Neural Network},
 year = {2025}
}

@inproceedings{peintner2025hyperhawkes,
  author       = {Andreas Peintner and
                  Amir Reza Mohammadi and
                  Michael M{\"{u}}ller and
                  Eva Zangerle},
  title        = {Hypergraph-based Temporal Modelling of Repeated Intent for Sequential
                  Recommendation},
  booktitle    = {Proceedings of the {ACM} on Web Conference 2025, {WWW} 2025, Sydney,
                  NSW, Australia, 28 April 2025- 2 May 2025},
  pages        = {3809--3818},
  publisher    = {{ACM}},
  year         = {2025},
  url          = {https://doi.org/10.1145/3696410.3714896},
  doi          = {10.1145/3696410.3714896}
}

@inproceedings{10.1145/3627043.3659540,
author = {Moscati, Marta and Strau\ss{}, Hannah and Jacobsen, Peer-Ole and Peintner, Andreas and Zangerle, Eva and Zentner, Marcel and Schedl, Markus},
title = {Emotion-Based Music Recommendation from Quality Annotations and Large-Scale User-Generated Tags},
year = {2024},
isbn = {9798400704338},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3627043.3659540},
doi = {10.1145/3627043.3659540},
abstract = {Emotions constitute an important aspect when listening to music. While manual annotations from user studies grounded in psychological research on music and emotions provide a well-defined and fine-grained description of the emotions evoked when listening to a music track, user-generated tags provide an alternative view stemming from large-scale data. In this work, we examine the relationship between these two emotional characterizations of music and analyze their impact on the performance of emotion-based music recommender systems individually and jointly. Our analysis shows that (i) the agreement between the two characterizations, as measured with Cohen’s κ coefficient and Kendall rank correlation, is often low, (ii) Leveraging the emotion profile based on the intensity of evoked emotions from high-quality annotations leads to performances that are stable across different recommendation algorithms; (iii) Simultaneously leveraging the emotion profiles based on high-quality and large-scale annotations allows to provide recommendations that are less exposed to the low accuracy that algorithms might reach when leveraging one type of data, only.},
booktitle = {Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization},
pages = {159–164},
numpages = {6},
keywords = {Annotation Study, Emotion-based Recommender Systems, Emotions, Music, Music Recommender Systems},
location = {Cagliari, Italy},
series = {UMAP '24}
}

@inproceedings{DBLP:conf/recsys/Peintner23,
  author       = {Andreas Peintner},
  title        = {Sequential Recommendation Models: {A} Graph-based Perspective},
  booktitle    = {Proceedings of the 17th {ACM} Conference on Recommender Systems, RecSys
                  2023, Singapore, Singapore, September 18-22, 2023},
  pages        = {1295--1299},
  publisher    = {{ACM}},
  year         = {2023},
  url          = {https://doi.org/10.1145/3604915.3608776},
  doi          = {10.1145/3604915.3608776},
  timestamp    = {Sun, 24 Sep 2023 15:46:34 +0200},
  biburl       = {https://dblp.org/rec/conf/recsys/Peintner23.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{DBLP:conf/recsys/PeintnerMZ23,
  author       = {Andreas Peintner and
                  Amir Reza Mohammadi and
                  Eva Zangerle},
  title        = {{SPARE:} Shortest Path Global Item Relations for Efficient Session-based
                  Recommendation},
  booktitle    = {Proceedings of the 17th {ACM} Conference on Recommender Systems, RecSys
                  2023, Singapore, Singapore, September 18-22, 2023},
  pages        = {58--69},
  publisher    = {{ACM}},
  year         = {2023},
  url          = {https://doi.org/10.1145/3604915.3608768},
  doi          = {10.1145/3604915.3608768}
}

@inproceedings{peintner:cars:2022,
 author = {Peintner, Andreas and Moscati, Marta and Parada-Cabaleiro, Emilia and Schedl, Markus and Zangerle, Eva},
 booktitle = {CARS: Workshop on Context-Aware Recommender Systems (RecSys ’22)},
 title = {Unsupervised Graph Embeddings for Session-based Recommendation with Item Features},
 year = {2022}
}

@inproceedings{mayerlRecognizingSongMood2021,
	title = {Recognizing Song Mood and Theme: Clustering-based Ensembles},
	booktitle = {Working Notes Proceedings of the MediaEval 2021 Workshop},
	publisher = {ceur-ws.org},
	author = {Mayerl, Maximilian and Vötter, Michael and Peintner, Andreas and Specht, Günther and Zangerle, Eva},
	month = {12},
	year = {2021},
}