@conference {180,
	title = {Cross-Domain Recommendations Using Attention and~Multitask Learning},
	booktitle = {Artificial Intelligence Applications and Innovations},
	year = {2025},
	publisher = {Springer Nature Switzerland},
	organization = {Springer Nature Switzerland},
	address = {Limassol},
	abstract = {In this study, we propose a novel deep learning method for cross-domain recommendations that effectively combines attention mechanisms, autoencoders, and multitask learning. Our approach leverages multiple datasets from diverse domains and incorporates domain-specific encoders, a shared self-attention mechanism, and a multilayer perceptron (MLP) to capture both intra-domain and inter-domain relationships. By jointly modeling these interactions, we improve recommendation accuracy across domains. Experimental results using the MovieLens dataset demonstrate that our proposed cross-domain recommendation system outperforms traditional approaches including matrix factorization, standard MLPs, and self-attention-based baselines.},
	isbn = {978-3-031-96228-8},
	doi = {https://doi.org/10.1007/978-3-031-96228-8_29},
	author = {Papachronopoulos, Gerasimos and Gkillas, Alexandros and Kosmopoulos, Dimitrios},
	editor = {Maglogiannis, Ilias and Iliadis, Lazaros and Andreou, Andreas and Papaleonidas, Antonios}
}
