@inproceedings{qayyum-etal-2025-teg,
    title = "{T}e{G}-{DR}ec: Inductive Text-Graph Learning for Unseen Node Scientific Dataset Recommendation",
    author = "Qayyum, Ammar  and
      Irnawan, Bassamtiano  and
      Fukumoto, Fumiyo  and
      Kamarudin, Latifah  and
      Go, Kentaro  and
      Suzuki, Yoshimi",
    editor = "Accomazzi, Alberto  and
      Ghosal, Tirthankar  and
      Grezes, Felix  and
      Lockhart, Kelly",
    booktitle = "Proceedings of the Third Workshop for Artificial Intelligence for Scientific Publications",
    month = dec,
    year = "2025",
    address = "Mumbai, India and virtual",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-ijcnlp-aacl/2025.wasp-main.12/",
    pages = "103--114",
    ISBN = "979-8-89176-310-4",
    abstract = "Scientific datasets are crucial for evaluating scientific research, and their number is increasing rapidly. Most scientific dataset recommendation systems use Information Retrieval (IR) methods that model semantics while overlooking interactions. Graph Neural Networks (GNNs) excel at handling interactions between entities but often overlook textual content, limiting their ability to generalise to unseen nodes. We propose TeG-DRec, a framework for scientific dataset recommendation that integrates GNNs and textual content via a subgraph generation module to ensure correct propagation throughout the model, enabling handling of unseen data. Experimental results on the dataset recommendation{'}s dataset show that our method outperformed the baselines for text-based IR and graph-based recommendation systems. Our source code is available at https://github.com/Maqif14/TeG-DRec.git"
}