Zobrazeno 1 - 9
of 9
pro vyhledávání: '"Mancino, Alberto Carlo Maria"'
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
Item recommendation (the task of predicting if a user may interact with new items from the catalogue in a recommendation system) and link prediction (the task of identifying missing links in a knowledge graph) have long been regarded as distinct prob
Externí odkaz:
http://arxiv.org/abs/2409.07433
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
Autor:
Gema, Aryo Pradipta, Leang, Joshua Ong Jun, Hong, Giwon, Devoto, Alessio, Mancino, Alberto Carlo Maria, Saxena, Rohit, He, Xuanli, Zhao, Yu, Du, Xiaotang, Madani, Mohammad Reza Ghasemi, Barale, Claire, McHardy, Robert, Harris, Joshua, Kaddour, Jean, van Krieken, Emile, Minervini, Pasquale
Maybe not. We identify and analyse errors in the popular Massive Multitask Language Understanding (MMLU) benchmark. Even though MMLU is widely adopted, our analysis demonstrates numerous ground truth errors that obscure the true capabilities of LLMs.
Externí odkaz:
http://arxiv.org/abs/2406.04127
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, 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, 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