Amir Reza Mohammadi, 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{murauer2022dtgrams,
  author       = {Amir Reza Mohammadi and
                  Amir{-}Hossein Karimi and
                  Mahdi Bohlouli and
                  Eva Zangerle and
                  G{\"{u}}nther Specht},
  booktitle = {Proceedings of the 34nd GI-Workshop Grundlagen von Datenbanksysteme (GvDB'23) },
  title     = {HPT4Rec: AutoML-based Hyperparameter Self-Tuning Framework for Session-based
                  Recommender Systems},
  year      = {2024},
}

@inproceedings{DBLP:conf/recsys/Mohammadi23,
  author       = {Amir Reza Mohammadi},
  title        = {Explainable Graph Neural Network Recommenders; Challenges and Opportunities},
  booktitle    = {Proceedings of the 17th {ACM} Conference on Recommender Systems, RecSys
                  2023, Singapore, Singapore, September 18-22, 2023},
  pages        = {1318--1324},
  publisher    = {{ACM}},
  year         = {2023},
  url          = {https://doi.org/10.1145/3604915.3608875},
  doi          = {10.1145/3604915.3608875},
  timestamp    = {Sun, 24 Sep 2023 15:46:34 +0200},
  biburl       = {https://dblp.org/rec/conf/recsys/Mohammadi23.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}
}