no code implementations • 8 Mar 2024 • Yashar Deldjoo, Tommaso Di Noia
In the evolving landscape of recommender systems, the integration of Large Language Models (LLMs) such as ChatGPT marks a new era, introducing the concept of Recommendation via LLM (RecLLM).
1 code implementation • 7 Mar 2024 • Matteo Attimonelli, Danilo Danese, Daniele Malitesta, Claudio Pomo, Giuseppe Gassi, Tommaso Di Noia
In this work, we introduce Ducho 2. 0, the latest stable version of our framework.
1 code implementation • 17 Oct 2023 • Daniele Malitesta, Claudio Pomo, Tommaso Di Noia
Graph neural networks (GNNs) have gained prominence in recommendation systems in recent years.
1 code implementation • 11 Sep 2023 • Daniele Malitesta, Giandomenico Cornacchia, Claudio Pomo, Felice Antonio Merra, Tommaso Di Noia, Eugenio Di Sciascio
Recommender systems (RSs) offer personalized navigation experiences on online platforms, but recommendation remains a challenging task, particularly in specific scenarios and domains.
1 code implementation • 7 Sep 2023 • Dario Di Palma, Giovanni Maria Biancofiore, Vito Walter Anelli, Fedelucio Narducci, Tommaso Di Noia, Eugenio Di Sciascio
Through thoroughly exploring ChatGPT's abilities in recommender systems, our study aims to contribute to the growing body of research on the versatility and potential applications of large language models.
1 code implementation • 24 Aug 2023 • Daniele Malitesta, Giandomenico Cornacchia, Claudio Pomo, Tommaso Di Noia
Multimodal-aware recommender systems (MRSs) exploit multimodal content (e. g., product images or descriptions) as items' side information to improve recommendation accuracy.
1 code implementation • 21 Aug 2023 • Daniele Malitesta, Claudio Pomo, Vito Walter Anelli, Alberto Carlo Maria Mancino, Eugenio Di Sciascio, Tommaso Di Noia
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).
1 code implementation • 17 Aug 2023 • Fatemeh Nazary, Yashar Deldjoo, Tommaso Di Noia
In essence, this paper bridges the gap between AI and healthcare, proposing a novel methodology for LLMs application in clinical decision support systems.
1 code implementation • 1 Aug 2023 • Vito Walter Anelli, Daniele Malitesta, Claudio Pomo, Alejandro Bellogín, Tommaso Di Noia, Eugenio Di Sciascio
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.
1 code implementation • 29 Jun 2023 • Daniele Malitesta, Giuseppe Gassi, Claudio Pomo, Tommaso Di Noia
Motivated by the outlined aspects, we propose \framework, a unified framework for the extraction of multimodal features in recommendation.
no code implementations • 21 Jun 2023 • Vincenzo Paparella, Vito Walter Anelli, Franco Maria Nardini, Raffaele Perego, Tommaso Di Noia
To our knowledge, there are no well-recognized strategies to decide which point should be selected on the frontier.
no code implementations • 28 Mar 2023 • Carmelo Ardito, Yashar Deldjoo, Tommaso Di Noia, Eugenio Di Sciascio, Fatemeh Nazary, Giovanni Servedio
In smart electrical grids, fault detection tasks may have a high impact on society due to their economic and critical implications.
no code implementations • 17 Jan 2023 • Domenico Lofù, Paolo Sorino, Tommaso Colafiglio, Caterina Bonfiglio, Fedelucio Narducci, Tommaso Di Noia, Eugenio Di Sciascio
Few articles are focused on mortality in MAFLD subjects, and none investigate how to predict a fatal outcome.
Explainable artificial intelligence Explainable Artificial Intelligence (XAI)
no code implementations • 4 Sep 2022 • Giovanni Maria Biancofiore, Yashar Deldjoo, Tommaso Di Noia, Eugenio Di Sciascio, Fedelucio Narducci
Interactive question answering is a recently proposed and increasingly popular solution that resides at the intersection of question answering and dialogue systems.
no code implementations • 13 Jul 2022 • Domenico Lofù, Pietro Di Gennaro, Pietro Tedeschi, Tommaso Di Noia, Eugenio Di Sciascio
A cost-effective and real-time framework is needed to detect the presence of drones in such cases.
no code implementations • 2 Mar 2022 • Vito Walter Anelli, Alejandro Bellogín, Tommaso Di Noia, Dietmar Jannach, Claudio Pomo
Moreover, we find that for none of the accuracy measurements any of the considered neural models led to the best performance.
no code implementations • 6 Feb 2022 • Yashar Deldjoo, Fatemeh Nazary, Arnau Ramisa, Julian McAuley, Giovanni Pellegrini, Alejandro Bellogin, Tommaso Di Noia
The textile and apparel industries have grown tremendously over the last few years.
no code implementations • 10 Nov 2021 • Tommaso Di Noia, Francesco Donini, Dietmar Jannach, Fedelucio Narducci, Claudio Pomo
With this work, we complement empirical research with a theoretical, domain-independent model of conversational recommendation.
no code implementations • 2 Sep 2021 • Vito Walter Anelli, Alejandro Bellogín, Tommaso Di Noia, Francesco Maria Donini, Vincenzo Paparella, Claudio Pomo
Explainable Recommendation has attracted a lot of attention due to a renewed interest in explainable artificial intelligence.
Explainable artificial intelligence Explainable Recommendation
no code implementations • 29 Jul 2021 • Vito Walter Anelli, Yashar Deldjoo, Tommaso Di Noia, Felice Antonio Merra
However, a key overlooked aspect has been the beyond-accuracy performance of APR, i. e., novelty, coverage, and amplification of popularity bias, considering that recent results suggest that BPR, the building block of APR, is sensitive to the intensification of biases and reduction of recommendation novelty.
no code implementations • 29 Jul 2021 • Vito Walter Anelli, Tommaso Di Noia, Eugenio Di Sciascio, Antonio Ferrara, Alberto Carlo Maria Mancino
In fact, in these cases we have that with a large number of high-quality features, the resulting models are more complex and difficult to train.
1 code implementation • 28 Jul 2021 • Vito Walter Anelli, Alejandro Bellogín, Tommaso Di Noia, Claudio Pomo
We replicate experiments from three papers that compare Neural Collaborative Filtering (NCF) and Matrix Factorization (MF), to extend the analysis to other evaluation dimensions.
1 code implementation • 3 Mar 2021 • Vito Walter Anelli, Alejandro Bellogín, Antonio Ferrara, Daniele Malitesta, Felice Antonio Merra, Claudio Pomo, Francesco Maria Donini, Tommaso Di Noia
Recommender Systems have shown to be an effective way to alleviate the over-choice problem and provide accurate and tailored recommendations.
no code implementations • 15 Dec 2020 • Vito Walter Anelli, Yashar Deldjoo, Tommaso Di Noia, Antonio Ferrara, Fedelucio Narducci
Recommender systems have shown to be a successful representative of how data availability can ease our everyday digital life.
no code implementations • 3 Oct 2020 • Vito Walter Anelli, Alejandro Bellogín, Yashar Deldjoo, Tommaso Di Noia, Felice Antonio Merra
However, while the single-step fast gradient sign method (FGSM) is the most explored perturbation strategy, multi-step (iterative) perturbation strategies, that demonstrated higher efficacy in the computer vision domain, have been highly under-researched in recommendation tasks.
no code implementations • 2 Oct 2020 • Vito Walter Anelli, Tommaso Di Noia, Daniele Malitesta, Felice Antonio Merra
However, since adversarial training techniques have proven to successfully robustify DNNs in preserving classification accuracy, to the best of our knowledge, two important questions have not been investigated yet: 1) How well can these defensive mechanisms protect the VRSs performance?
no code implementations • 17 Aug 2020 • Vito Walter Anelli, Yashar Deldjoo, Tommaso Di Noia, Antonio Ferrara, Fedelucio Narducci
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.
no code implementations • 17 Jul 2020 • Vito Walter Anelli, Yashar Deldjoo, Tommaso Di Noia, Antonio Ferrara
In order to address these issues, Federated Learning (FL) has been recently proposed as a means to build ML models based on private datasets distributed over a large number of clients, while preventing data leakage.
1 code implementation • 20 May 2020 • Yashar Deldjoo, Tommaso Di Noia, Felice Antonio Merra
Latent-factor models (LFM) based on collaborative filtering (CF), such as matrix factorization (MF) and deep CF methods, are widely used in modern recommender systems (RS) due to their excellent performance and recommendation accuracy.
1 code implementation • 11 Sep 2019 • Vito Walter Anelli, Tommaso Di Noia, Eugenio Di Sciascio, Azzurra Ragone, Joseph Trotta
By relying on the information encoded in the original knowledge graph, we have also evaluated the semantic accuracy and robustness for the knowledge-aware interpretability of the final model.
no code implementations • 5 Sep 2019 • Vito Walter Anelli, Tommaso Di Noia, Eugenio Di Sciascio, Claudio Pomo, Azzurra Ragone
Hyper-parameters tuning is a crucial task to make a model perform at its best.
no code implementations • 21 Aug 2019 • Yashar Deldjoo, Tommaso Di Noia, Felice Antonio Merra
While previous works have focused on evaluating shilling attack strategies from a global perspective paying particular attention to the effect of the size of attacks and attacker's knowledge, in this work we explore the effectiveness of shilling attacks under novel aspects.
no code implementations • 20 Aug 2019 • Vito Walter Anelli, Yashar Deldjoo, Tommaso Di Noia, Antonio Ferrara
With the wealth of information produced by social networks, smartphones, medical or financial applications, speculations have been raised about the sensitivity of such data in terms of users' personal privacy and data security.
no code implementations • 19 Aug 2019 • Yashar Deldjoo, Vito Walter Anelli, Hamed Zamani, Alejandro Bellogin, Tommaso Di Noia
We present a probabilistic framework based on generalized cross entropy to evaluate fairness of recommender systems under this perspective, where we show that the proposed framework is flexible and explanatory by allowing to incorporate domain knowledge (through an ideal fair distribution) that can help to understand which item or user aspects a recommendation algorithm is over- or under-representing.
no code implementations • 17 Jul 2018 • Vito Bellini, Angelo Schiavone, Tommaso Di Noia, Azzurra Ragone, Eugenio Di Sciascio
Recommender Systems have been widely used to help users in finding what they are looking for thus tackling the information overload problem.
1 code implementation • 13 Jul 2018 • Vito Bellini, Angelo Schiavone, Tommaso Di Noia, Azzurra Ragone, Eugenio Di Sciascio
In the last years, deep learning has shown to be a game-changing technology in artificial intelligence thanks to the numerous successes it reached in diverse application fields.
3 code implementations • 11 Jul 2018 • Vito Walter Anelli, Tommaso Di Noia, Eugenio Di Sciascio, Azzurra Ragone, Joseph Trotta
Items popularity is a strong signal in recommendation algorithms.
1 code implementation • 11 Jul 2018 • Vito Walter Anelli, Joseph Trotta, Tommaso Di Noia, Eugenio Di Sciascio, Azzurra Ragone
Similarity measures play a fundamental role in memory-based nearest neighbors approaches.
1 code implementation • Semantic Web Journal 2017 • Petar Ristoski, Jessica Rosati, Tommaso Di Noia, Renato De Leone, Heiko Paulheim
Linked Open Data has been recognized as a valuable source for background information in many data mining and information retrieval tasks.
Ranked #2 on Node Classification on BGS
no code implementations • 24 Jun 2017 • Vito Bellini, Vito Walter Anelli, Tommaso Di Noia, Eugenio Di Sciascio
In the last decade, driven also by the availability of an unprecedented computational power and storage capabilities in cloud environments we assisted to the proliferation of new algorithms, methods, and approaches in two areas of artificial intelligence: knowledge representation and machine learning.