Zobrazeno 1 - 10
of 89
pro vyhledávání: '"Ferrara Antonio"'
Autor:
Mancino, Alberto Carlo Maria, Bufi, Salvatore, Di Fazio, Angela, Malitesta, Daniele, Pomo, Claudio, Ferrara, Antonio, Di Noia, Tommaso
Thanks to the great interest posed by researchers and companies, recommendation systems became a cornerstone of machine learning applications. However, concerns have arisen recently about the need for reproducibility, making it challenging to identif
Externí odkaz:
http://arxiv.org/abs/2410.22972
Pairwise comparisons based on human judgements are an effective method for determining rankings of items or individuals. However, as human biases perpetuate from pairwise comparisons to recovered rankings, they affect algorithmic decision making. In
Externí odkaz:
http://arxiv.org/abs/2408.13034
Autor:
Bufi, Salvatore, Mancino, Alberto Carlo Maria, Ferrara, Antonio, Malitesta, Daniele, Di Noia, Tommaso, Di Sciascio, Eugenio
The recent integration of Graph Neural Networks (GNNs) into recommendation has led to a novel family of Collaborative Filtering (CF) approaches, namely Graph Collaborative Filtering (GCF). Following the same GNNs wave, recommender systems exploiting
Externí odkaz:
http://arxiv.org/abs/2403.20095
Liberalism-oriented political philosophy reasons that all individuals should be treated equally independently of their protected characteristics. Related work in machine learning has translated the concept of \emph{equal treatment} into terms of \emp
Externí odkaz:
http://arxiv.org/abs/2303.08040
Network-based people recommendation algorithms are widely employed on the Web to suggest new connections in social media or professional platforms. While such recommendations bring people together, the feedback loop between the algorithms and the cha
Externí odkaz:
http://arxiv.org/abs/2205.06048
Autor:
Anelli, Vito Walter, Di Noia, Tommaso, Di Sciascio, Eugenio, Ferrara, Antonio, Mancino, Alberto Carlo Maria
Deep Learning and factorization-based collaborative filtering recommendation models have undoubtedly dominated the scene of recommender systems in recent years. However, despite their outstanding performance, these methods require a training time pro
Externí odkaz:
http://arxiv.org/abs/2107.14290
Autor:
Anelli, Vito Walter, Bellogín, Alejandro, Ferrara, Antonio, Malitesta, Daniele, Merra, Felice Antonio, Pomo, Claudio, Donini, Francesco Maria, Di Noia, Tommaso
Publikováno v:
Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval 2021
Recommender Systems have shown to be an effective way to alleviate the over-choice problem and provide accurate and tailored recommendations. However, the impressive number of proposed recommendation algorithms, splitting strategies, evaluation proto
Externí odkaz:
http://arxiv.org/abs/2103.02590
Autor:
Anelli, Vito Walter, Deldjoo, Yashar, Di Noia, Tommaso, Ferrara, Antonio, Narducci, Fedelucio
Recommender systems have shown to be a successful representative of how data availability can ease our everyday digital life. However, data privacy is one of the most prominent concerns in the digital era. After several data breaches and privacy scan
Externí odkaz:
http://arxiv.org/abs/2012.11328
Autor:
Anelli, Vito Walter, Deldjoo, Yashar, Di Noia, Tommaso, Ferrara, Antonio, Narducci, Fedelucio
Recommendation services are extensively adopted in several user-centered applications as a tool to alleviate the information overload problem and help users in orienteering in a vast space of possible choices. In such scenarios, data ownership is a c
Externí odkaz:
http://arxiv.org/abs/2008.07192
In Machine Learning scenarios, privacy is a crucial concern when models have to be trained with private data coming from users of a service, such as a recommender system, a location-based mobile service, a mobile phone text messaging service providin
Externí odkaz:
http://arxiv.org/abs/2007.08893