Zobrazeno 1 - 10
of 4 224
pro vyhledávání: '"Medda, A."'
Autor:
Malitesta, Daniele, Medda, Giacomo, Purificato, Erasmo, Boratto, Ludovico, Malliaros, Fragkiskos D., Marras, Mirko, De Luca, Ernesto William
Diffusion-based recommender systems have recently proven to outperform traditional generative recommendation approaches, such as variational autoencoders and generative adversarial networks. Nevertheless, the machine learning literature has raised se
Externí odkaz:
http://arxiv.org/abs/2409.04339
Recent developments in recommendation have harnessed the collaborative power of graph neural networks (GNNs) in learning users' preferences from user-item networks. Despite emerging regulations addressing fairness of automated systems, unfairness iss
Externí odkaz:
http://arxiv.org/abs/2408.12208
In this paper, we introduce the Dynamic Modularity-Spectral Algorithm (DynMSA), a novel approach to identify clusters of stocks with high intra-cluster correlations and low inter-cluster correlations by combining Random Matrix Theory with modularity
Externí odkaz:
http://arxiv.org/abs/2407.04500
Autor:
Ravagnani, Adele, Lillo, Fabrizio, Deriu, Paola, Mazzarisi, Piero, Medda, Francesca, Russo, Antonio
Identification of market abuse is an extremely complicated activity that requires the analysis of large and complex datasets. We propose an unsupervised machine learning method for contextual anomaly detection, which allows to support market surveill
Externí odkaz:
http://arxiv.org/abs/2403.00707
Efforts in the recommendation community are shifting from the sole emphasis on utility to considering beyond-utility factors, such as fairness and robustness. Robustness of recommendation models is typically linked to their ability to maintain the or
Externí odkaz:
http://arxiv.org/abs/2401.13823
Autor:
Piero Mazzarisi, Adele Ravagnani, Paola Deriu, Fabrizio Lillo, Francesca Medda, Antonio Russo
Publikováno v:
EPJ Data Science, Vol 13, Iss 1, Pp 1-44 (2024)
Abstract Identifying market abuse activity from data on investors’ trading activity is very challenging both for the data volume and for the low signal to noise ratio. Here we propose two complementary unsupervised machine learning methods to suppo
Externí odkaz:
https://doaj.org/article/26aa39bf79f641ef94dd78c680f72386
In recommendation literature, explainability and fairness are becoming two prominent perspectives to consider. However, prior works have mostly addressed them separately, for instance by explaining to consumers why a certain item was recommended or m
Externí odkaz:
http://arxiv.org/abs/2308.12083
Nowadays, research into personalization has been focusing on explainability and fairness. Several approaches proposed in recent works are able to explain individual recommendations in a post-hoc manner or by explanation paths. However, explainability
Externí odkaz:
http://arxiv.org/abs/2304.06182
Autor:
Morin, Diego Gonzalez, Medda, Daniele, Iossifides, Athanasios, Chatzimisios, Periklis, Armada, Ana Garcia, Villegas, Alvaro, Perez, Pablo
In recent years, advances in immersive multimedia technologies, such as extended reality (XR) technologies, have led to more realistic and user-friendly devices. However, these devices are often bulky and uncomfortable, still requiring tether connect
Externí odkaz:
http://arxiv.org/abs/2301.11217
Autor:
Mazzarisi, Piero, Ravagnani, Adele, Deriu, Paola, Lillo, Fabrizio, Medda, Francesca, Russo, Antonio
Identifying market abuse activity from data on investors' trading activity is very challenging both for the data volume and for the low signal to noise ratio. Here we propose two complementary unsupervised machine learning methods to support market s
Externí odkaz:
http://arxiv.org/abs/2212.05912