Andreas Peintner, Msc.

@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}
}

@article{10.1145/3701764,
author = {Peintner, Andreas and Mohammadi, Amir Reza and Zangerle, Eva},
title = {Efficient Session-based Recommendation with Contrastive Graph-based Shortest Path Search},
year = {2024},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3701764},
doi = {10.1145/3701764},
abstract = {Session-based recommendation aims to predict the next item based on a set of anonymous sessions. Capturing user intent from a short interaction sequence imposes a variety of challenges since no user profiles are available and interaction data is naturally sparse. Recent approaches relying on graph neural networks (GNNs) for session-based recommendation use global item relations to explore collaborative information from different sessions. These methods capture the topological structure of the graph and rely on multi-hop information aggregation in GNNs to exchange information along edges. Consequently, graph-based models suffer from noisy item relations in the training data and introduce high complexity for large item catalogs. We propose to explicitly model the multi-hop information aggregation mechanism over multiple layers via shortest-path edges based on knowledge from the sequential recommendation domain. Our approach does not require multiple layers to exchange information and ignores unreliable item-item relations. Furthermore, to address inherent data sparsity, we are the first to apply supervised contrastive learning by mining data-driven positive and hard negative item samples from the training data. Extensive experiments on four different datasets show that the proposed approach outperforms almost all of the state-of-the-art methods.},
note = {Just Accepted},
journal = {ACM Trans. Recomm. Syst.},
month = oct,
keywords = {Recommender Systems, Session-based Recommendation, Graph Neural Network, Supervised Contrastive Learning}
}

@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},
}