Here you can find a consolidated (a.k.a. slowly updated) list of my publications. A frequently updated (and possibly noisy) list of works is available on my Google Scholar profile.
Please find below a short list of highlight publications for my recent activity.
Dukic, Haris; Mokarizadeh, Shahab; Deligiorgis, Georgios; Sepe, Pierpaolo; Bacciu, Davide; Trincavelli, Marco Inductive-Transductive Learning for Very Sparse Fashion Graphs Journal Article In: Neurocomputing, 2022, ISSN: 0925-2312. Sattar, Asma; Bacciu, Davide Graph Neural Network for Context-Aware Recommendation Journal Article In: Neural Processing Letters, 2022. Dukic, Haris; Deligiorgis, Georgios; Sepe, Pierpaolo; Bacciu, Davide; Trincavelli, Marco Inductive learning for product assortment graph completion Conference Proceedings of the 29th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2021), 2021. Sattar, Asma; Bacciu, Davide Context-aware Graph Convolutional Autoencoder Conference Proceedings of the 16th International Work Conference on Artificial Neural Networks (IWANN 2021), vol. 12862, LNCS Springer, 2021.
@article{DUKIC2022,
title = {Inductive-Transductive Learning for Very Sparse Fashion Graphs},
author = {Haris Dukic and Shahab Mokarizadeh and Georgios Deligiorgis and Pierpaolo Sepe and Davide Bacciu and Marco Trincavelli},
doi = {https://doi.org/10.1016/j.neucom.2022.06.050},
issn = {0925-2312},
year = {2022},
date = {2022-06-27},
urldate = {2022-06-27},
journal = {Neurocomputing},
abstract = {The assortments of global retailers are composed of hundreds of thousands of products linked by several types of relationships such as style compatibility, ”bought together”, ”watched together”, etc. Graphs are a natural representation for assortments, where products are nodes and relations are edges. Style compatibility relations are produced manually and do not cover the whole graph uniformly. We propose to use inductive learning to enhance a graph encoding style compatibility of a fashion assortment, leveraging rich node information comprising textual descriptions and visual data. Then, we show how the proposed graph enhancement substantially improves the performance on transductive tasks with a minor impact on graph sparsity. Although demonstrated in a challenging and novel industrial application case, the approach we propose is general enough to be applied to any node-level or edge-level prediction task in very sparse, large-scale networks.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{nokey,
title = {Graph Neural Network for Context-Aware Recommendation},
author = {Asma Sattar and Davide Bacciu},
doi = {10.1007/s11063-022-10917-3},
year = {2022},
date = {2022-06-22},
urldate = {2022-06-22},
journal = {Neural Processing Letters},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@conference{Dukic2021,
title = {Inductive learning for product assortment graph completion},
author = {Haris Dukic and Georgios Deligiorgis and Pierpaolo Sepe and Davide Bacciu and Marco Trincavelli},
editor = {Michel Verleysen},
doi = {10.14428/esann/2021.ES2021-73},
year = {2021},
date = {2021-10-06},
urldate = {2021-10-06},
booktitle = {Proceedings of the 29th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2021)},
pages = {129-134},
abstract = { Global retailers have assortments that contain hundreds of thousands of products that can be linked by several types of relationships like style compatibility, "bought together", "watched together", etc. Graphs are a natural representation for assortments, where products are nodes and relations are edges. Relations like style compatibility are often produced by a manual process and therefore do not cover uniformly the whole graph. We propose to use inductive learning to enhance a graph encoding style compatibility of a fashion assortment, leveraging rich node information comprising textual descriptions and visual data. Then, we show how the proposed graph enhancement improves substantially the performance on transductive tasks with a minor impact on graph sparsity. },
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
@conference{Sattar2021,
title = {Context-aware Graph Convolutional Autoencoder},
author = {Asma Sattar and Davide Bacciu
},
doi = {10.1007/978-3-030-85030-2_23},
year = {2021},
date = {2021-06-16},
urldate = {2021-06-16},
booktitle = {Proceedings of the 16th International Work Conference on Artificial Neural Networks (IWANN 2021)},
volume = {12862},
pages = { 279-290},
publisher = {Springer},
series = {LNCS},
abstract = {Recommendation problems can be addressed as link prediction tasks in a bipartite graph between user and item nodes, labelled with rating on edges. Existing matrix completion approaches model the user’s opinion on items by ignoring context information that can instead be associated with the edges of the bipartite graph. Context is an important factor to be considered as it heavily affects opinions and preferences. Following this line of research, this paper proposes a graph convolutional auto-encoder approach which considers users’ opinion on items as well as the static node features and context information on edges. Our graph encoder produces a representation of users and items from the perspective of context, static features, and rating opinion. The empirical analysis on three real-world datasets shows that the proposed approach outperforms recent state-of-the-art recommendation systems.},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}