Zobrazeno 1 - 10
of 342
pro vyhledávání: '"Sciascio, Eugenio"'
Autor:
Malitesta, Daniele, Pomo, Claudio, Anelli, Vito Walter, Mancino, Alberto Carlo Maria, Di Noia, Tommaso, Di Sciascio, Eugenio
Recently, graph neural networks (GNNs)-based recommender systems have encountered great success in recommendation. As the number of GNNs approaches rises, some works have started questioning the theoretical and empirical reasons behind their superior
Externí odkaz:
http://arxiv.org/abs/2408.11762
The integration of Large Language Models (LLMs) into healthcare diagnostics offers a promising avenue for clinical decision-making. This study outlines the development of a novel method for zero-shot/few-shot in-context learning (ICL) by integrating
Externí odkaz:
http://arxiv.org/abs/2405.06270
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
Autor:
Malitesta, Daniele, Cornacchia, Giandomenico, Pomo, Claudio, Merra, Felice Antonio, Di Noia, Tommaso, Di Sciascio, Eugenio
Recommender systems (RSs) offer personalized navigation experiences on online platforms, but recommendation remains a challenging task, particularly in specific scenarios and domains. Multimodality can help tap into richer information sources and con
Externí odkaz:
http://arxiv.org/abs/2309.05273
Autor:
Di Palma, Dario, Biancofiore, Giovanni Maria, Anelli, Vito Walter, Narducci, Fedelucio, Di Noia, Tommaso, Di Sciascio, Eugenio
Large Language Models (LLMs) have recently shown impressive abilities in handling various natural language-related tasks. Among different LLMs, current studies have assessed ChatGPT's superior performance across manifold tasks, especially under the z
Externí odkaz:
http://arxiv.org/abs/2309.03613
Autor:
Malitesta, Daniele, Pomo, Claudio, Anelli, Vito Walter, Mancino, Alberto Carlo Maria, Di Sciascio, Eugenio, Di Noia, Tommaso
The successful integration of graph neural networks into recommender systems (RSs) has led to a novel paradigm in collaborative filtering (CF), graph collaborative filtering (graph CF). By representing user-item data as an undirected, bipartite graph
Externí odkaz:
http://arxiv.org/abs/2308.10778
Autor:
Anelli, Vito Walter, Malitesta, Daniele, Pomo, Claudio, Bellogín, Alejandro, Di Noia, Tommaso, Di Sciascio, Eugenio
The success of graph neural network-based models (GNNs) has significantly advanced recommender systems by effectively modeling users and items as a bipartite, undirected graph. However, many original graph-based works often adopt results from baselin
Externí odkaz:
http://arxiv.org/abs/2308.00404
Autor:
Ardito, Carmelo, Deldjoo, Yashar, Di Noia, Tommaso, Di Sciascio, Eugenio, Nazary, Fatemeh, Servedio, Giovanni
In smart electrical grids, fault detection tasks may have a high impact on society due to their economic and critical implications. In the recent years, numerous smart grid applications, such as defect detection and load forecasting, have embraced da
Externí odkaz:
http://arxiv.org/abs/2303.18136
Autor:
Cornacchia, Giandomenico, Anelli, Vito Walter, Narducci, Fedelucio, Ragone, Azzurra, Di Sciascio, Eugenio
Current AI regulations require discarding sensitive features (e.g., gender, race, religion) in the algorithm's decision-making process to prevent unfair outcomes. However, even without sensitive features in the training set, algorithms can persist in
Externí odkaz:
http://arxiv.org/abs/2302.08204
Autor:
Cornacchia, Giandomenico, Anelli, Vito Walter, Narducci, Fedelucio, Ragone, Azzurra, Di Sciascio, Eugenio
The increasing application of Artificial Intelligence and Machine Learning models poses potential risks of unfair behavior and, in light of recent regulations, has attracted the attention of the research community. Several researchers focused on seek
Externí odkaz:
http://arxiv.org/abs/2302.08158