Tommaso Di Noia

Tommaso Di Noia
Politecnico di Bari | Poliba · Dipartimento di Ingegneria Elettrica e dell’Informazione

Professor

About

354
Publications
53,763
Reads
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4,532
Citations
Citations since 2016
165 Research Items
2593 Citations
20162017201820192020202120220100200300400500
20162017201820192020202120220100200300400500
20162017201820192020202120220100200300400500
20162017201820192020202120220100200300400500
Additional affiliations
May 2002 - present
Politecnico di Bari
Position
  • Professor (Assistant)

Publications

Publications (354)
Preprint
Full-text available
Graph collaborative filtering approaches learn refined users' and items' node representations by iteratively aggregating the informative content (called messages) coming from neighbor nodes into each ego node. Unfortunately, not all interactions (i.e., graph edges) may be equally important to the users and items involved. As this indiscriminate mes...
Article
Background: Recently, a tool based on two different artificial neural networks has been developed. The first network predicts kidney failure (KF) development while the second predicts the time frame to reach this outcome. In this study, we conducted a post-hoc analysis to evaluate the discordant results obtained by the tool. Methods: The tool pe...
Conference Paper
Full-text available
Due to their economic and significant importance, fault detection tasks in intelligent electrical grids are vital. Although numerous smart grid (SG) applications, such as fault detection and load forecasting, have adopted data-driven approaches, the robustness and security of these data-driven algorithms have not been widely examined. One of the gr...
Preprint
Full-text available
Graph convolutional networks (GCNs) have recently been shown to improve the recommendation accuracy of collaborative filtering algorithms. Their message-passing schema refines user and item node representation by aggregating the informative content from the neighborhood. However, after multiple hops, noisy contributions can flatten the differences...
Conference Paper
Full-text available
Pre-trained CNN models are frequently employed for a variety of machine learning tasks, including visual recognition and recommendation. We are interested in examining the application of attacks generated by adversarial machine learning techniques to the vertical domain of fashion and retail products. Specifically, the present work focuses on the r...
Preprint
Full-text available
Question answering systems are recognized as popular and frequently effective means of information seeking on the web. In such systems, information seekers can receive a concise response to their query by presenting their questions in natural language. Interactive question answering is a recently proposed and increasingly popular solution that resi...
Conference Paper
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 data-driven methodologies. The purpose of this study is to investigate the challenges associated with...
Preprint
Full-text available
Due to their economic and significant importance, fault detection tasks in intelligent electrical grids (fault detection, fault type, and fault location classifications) are vital. Although numerous smart grid (SG) applications, such as fault detection and load forecasting, have adopted data-driven approaches, the robustness and security of these d...
Article
Full-text available
In electrical grids, fault diagnosis (fault type and fault location classifications) are critical due to their economic and important implications. Numerous smart grid applications have embraced data-driven methodologies. While the majority of the work in this topic has been on increasing the predicted accuracy of machine-learning model for fault d...
Article
Recommender systems help users find items of interest in situations of information overload in a personalized way, using needs and preferences of individual users. In conversational recommendation approaches, the system acquires needs and preferences in an interactive, multi-turn dialog. This is usually driven by incrementally asking users about th...
Preprint
Full-text available
Safety and security issues for Critical Infrastructures (CI) are growing as attackers increasingly adopt drones as an attack vector flying in sensitive airspace, such as airports, military bases, city centres, and crowded places. The rapid proliferation of drones for merchandise, shipping recreations activities, and other commercial applications po...
Conference Paper
Full-text available
Research on recommender systems algorithms, like other areas of applied machine learning, is largely dominated by efforts to improve the state-of-the-art, typically in terms of accuracy measures. Several recent research works however indicate that the reported improvements over the years sometimes "don't add up", and that methods that were publishe...
Article
Full-text available
Point-of-interest (POI) recommendation is an essential service to location-based social networks (LBSNs), benefiting both users providing them the chance to explore new locations and businesses by discovering new potential customers. These systems learn the preferences of users and their mobility patterns to generate relevant POI recommendations. P...
Article
Background and objective: Aim of nephrologists is to delay the outcome and reduce the number of patients undergoing renal failure (RF) by applying prevention protocols and accurately monitoring chronic kidney disease (CKD) patients. General practitioners and nephrologists are involved in the first and in the late stages of the disease, respectivel...
Article
BACKGROUND AND AIMS Idiopathic Immunoglobulin A nephropathy (IgAN) is the most common biopsy-proven glomerulonephritis in the world. Approximately 40% of IgAN patients reach renal failure (RF) 20 years after their kidney biopsy. The high prevalence of RF shows that IgAN has a significant economic impact in the countries because renal replacement th...
Chapter
Adversarial machine learning is the research field investigating vulnerabilities inherent to machine learning systems’ design and ways to defend against them. Recently, recommender systems have been shown vulnerable to adversarial attacks that force the models to produce misleading recommendations. For instance, adversaries can attempt to push targ...
Article
Full-text available
Recommendation services have been extensively adopted in various user-centered applications to help users navigate a vast space of possible choices. In such scenarios, data ownership is a crucial concern since users may not be willing to share their sensitive preferences (e.g., visited locations, read books, bought items) with a central server. Unf...
Article
Full-text available
Dietary behaviour is a core element in diabetes self-management. There are no remarkable differences between nutritional guidelines for people with type 2 diabetes and healthy eating recommendations for the general public. This study aimed to evaluate dietary differences between subjects with and without diabetes and to describe any emerging dietar...
Article
In this narrative review, we focus on the application of artificial intelligence in the clinical history of patients with glomerular disease, digital pathology in kidney biopsy, renal ultrasonography imaging, and prediction of chronic kidney disease (CKD). With the development of natural language processing, the clinical history of a patient can be...
Preprint
Full-text available
Research on recommender systems algorithms, like other areas of applied machine learning, is largely dominated by efforts to improve the state-of-the-art, typically in terms of accuracy measures. Several recent research works however indicate that the reported improvements over the years sometimes "don't add up", and that methods that were publishe...
Article
Full-text available
Background Diet and social determinants influence the state of human health. In older adults, the presence of social, physical and psychological barriers increases the probability of deprivation. This study investigated the relationship between social deprivation and eating habits in non-institutionalized older adults from Southern Italy, and ident...
Preprint
Full-text available
The textile and apparel industries have grown tremendously over the last years. Customers no longer have to visit many stores, stand in long queues, or try on garments in dressing rooms as millions of products are now available in online catalogs. However, given the plethora of options available, an effective recommendation system is necessary to p...
Article
Full-text available
The digital transformation of the construction sector is also involving cultural and architectural heritage conservation management to solve criticalities of information exchange in refurbishment/restoration, from the preliminary steps until the execution and monitoring of interventions. Nevertheless, time and resources required to complete digital...
Chapter
Full-text available
When customers’ choices may depend on the visual appearance of products (e.g., fashion), visually-aware recommender systems (VRSs) have been shown to provide more accurate preference predictions than pure collaborative models. To refine recommendations, recent VRSs have tried to recognize the influence of each item’s visual characteristic on users’...
Chapter
The pervasiveness of modern machine learning algorithms exposes users to new vulnerabilities: violation of sensitive information stored in the training data and wrong model behaviors caused by adversaries. State-of-the-art approaches to prevent such behaviors are usually based on Differential Privacy (DP) and Adversarial Training (AT). DP is a rigo...
Article
In this report, we offer a brief overview of the contributions and takeaways from the Joint KaRS & ComplexRec Workshop, co-located with the 15 th edition of the ACM RecSys in Amsterdam, The Netherlands. With this workshop, we aimed to merge the main objectives envisioned for the 3 rd Edition of the Workshop of Knowledge-aware and Conversational Rec...
Preprint
Full-text available
When customers' choices may depend on the visual appearance of products (e.g., fashion), visually-aware recommender systems (VRSs) have shown to provide more accurate preference predictions than pure collaborative models. To refine recommendations, recent VRSs have tried to recognize the influence of each item's visual characteristic on users' pref...
Preprint
Full-text available
Recommender systems are software applications that help users find items of interest in situations of information overload in a personalized way, using knowledge about the needs and preferences of individual users. In conversational recommendation approaches, these needs and preferences are acquired by the system in an interactive, multi-turn dialo...
Preprint
Full-text available
Recommender systems (RSs) employ user-item feedback, e.g., ratings , to match customers to personalized lists of products. Approaches to top-k recommendation mainly rely on Learning-To-Rank algorithms and, among them, the most widely adopted is Bayesian Personalized Ranking (BPR), which bases on a pair-wise optimization approach. Recently, BPR has...
Conference Paper
Full-text available
Explainable Recommendation has attracted a lot of attention due to a renewed interest in explainable artificial intelligence. In particular, post-hoc approaches have proved to be the most easily applicable ones to increasingly complex recommendation models, which are then treated as black boxes. The most recent literature has shown that for post-ho...
Conference Paper
Full-text available
Recommender systems (RSs) have widely grown thanks to the outstanding capability of providing users with accurate and tailored recommendations. Recently, public awareness and new regulations forced RS researchers and practitioners to study solutions to user privacy endangerment. This tutorial will guide the attendees through the possible threats an...
Conference Paper
Full-text available
Collaborative filtering models based on matrix factorization and learned similarities using Artificial Neural Networks (ANNs) have gained significant attention in recent years. This is, in part, because ANNs have demonstrated very good results in a wide variety of recommendation tasks. However, the introduction of ANNs within the recommendation eco...
Conference Paper
Full-text available
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 protocols, metrics, and tasks, has made rigorous experimental evaluation particularly challenging. ELLIOT...
Conference Paper
Full-text available
The paper introduces Visual-Elliot (V-Elliot), a reproducibility framework for Visual Recommendation systems (VRSs) based on Elliot. framework provides the widest set of VRSs compared to other recommendation frameworks in the literature (i.e., 6 state-of-the-art models which have been commonly employed as baselines in recent works). The framework p...
Preprint
Full-text available
Explainable Recommendation has attracted a lot of attention due to a renewed interest in explainable artificial intelligence. In particular, post-hoc approaches have proved to be the most easily applicable ones to increasingly complex recommendation models, which are then treated as black-boxes. The most recent literature has shown that for post-ho...
Conference Paper
Full-text available
Visually-aware recommender systems (VRSs) integrate products' image features with historical users' feedback to enhance recommendation performance. Such models have shown to be very effective in different domains, ranging from fashion, food, to point-of-interest. However, test-time adversarial attack strategies have recently unveiled severe securit...
Conference Paper
Full-text available
Collaborative filtering recommender systems (CF-RSs) employ user-item feedback, e.g., ratings, purchases, or reviews, to harmonize similarities among customers and produce personalized lists of products. Being based on the benevolence of other customers, CF-RSs are vulnerable to Shilling Attacks, i.e., fake profiles injected on the platform by adve...
Conference Paper
Full-text available
Recently, recommendation systems have been proven to be susceptible to malicious perturbations of the model weights. To overcome this vulnerability, Adversarial Regularization emerged as one of the most effective solutions. Interestingly, the technique not only robustifies the model, but also significantly increases its accuracy. To date, unfortuna...
Chapter
Brain Computer Interfaces can enable engaging interactions between different art forms such as music, dance, painting. Building on this, we present a demo of a biofeedback system: a dancer wearing a NeuroSky headset adapts her performance according to the music she listens to. The same music has been generated by a music-composition software depend...
Chapter
New and evolving threats emerge every day in the e-Health industry. The safety of e-Health’s telemonitoring systems is becoming a prominent task. In this work, starting from a CADS (Cyberattack Detection System) model that uses artificial intelligence techniques to detect anomalies, we focus on the activity of interacting with data. Using a User In...
Chapter
In the Brain Computer Interface domain, studies on EEG represent a huge field of interest. Interactive systems that exploit low cost electroencephalographs to control machines are gaining momentum. Such technologies can be useful in the field of music and assisted composition. In this paper, a system that aims to generate four-part polyphonies is p...
Preprint
Full-text available
Recommender systems (RSs) employ user-item feedback, e.g., ratings, to match customers to personalized lists of products. Approaches to top-k recommendation mainly rely on Learning-To-Rank algorithms and, among them, the most widely adopted is Bayesian Personalized Ranking (BPR), which bases on a pair-wise optimization approach. Recently, BPR has b...
Preprint
Full-text available
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 proportional to the size of the embeddings and it further increases when also side information is consi...
Preprint
Full-text available
Collaborative filtering models based on matrix factorization and learned similarities using Artificial Neural Networks (ANNs) have gained significant attention in recent years. This is, in part, because ANNs have demonstrated good results in a wide variety of recommendation tasks. The introduction of ANNs within the recommendation ecosystem has bee...
Article
Full-text available
One common characteristic of research works focused on fairness evaluation (in machine learning) is that they call for some form of parity (equality) either in treatment – meaning they ignore the information about users’ memberships in protected classes during training – or in impact – by enforcing proportional beneficial outcomes to users in diffe...
Conference Paper
Full-text available
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 protocols, metrics, and tasks, has made rigorous experimental evaluation particularly challenging. Puzzle...
Article
Full-text available
The impact of data characteristics on the performance of classical recommender systems has been recently investigated and produced fruitful results about the relationship they have with recommendation accuracy. This work provides a systematic study on the impact of broadly chosen data characteristics (DCs) of recommender systems. This is applied to...
Conference Paper
Full-text available
Visually-aware recommender systems (VRSs) enhance the semantics of user-item interactions with visual features extracted from item images when they are available. Traditionally, VRSs leverage the representational power of pretrained convolutional neural networks (CNNs) to perform the item recommendation task. The adoption of CNNs is mainly attribut...
Article
Background During the last twenty years many tools, based on mathematical models, have been developed to predict ESKD at the time of kidney biopsy in patients with IgAN. The main limitation of these tools is the time frame to reach the ESKD. Recently, we have developed a Clinical Decision Support System (CDSS) (KI 2020) which includes 6 variables a...
Chapter
The SARS-CoV-2 pandemic has brought unexpected new scenarios in patient-care journeys and has accelerated this innovative process in the healthcare sector, demonstrating the importance of a systemic rethinking of remote care, mostly when patients are discharged from the hospital and continue their therapies at home in autonomy. The possibility to r...
Article
Full-text available
Recommender systems (RSs) have attained exceptional performance in learning users' preferences and finding the most suitable products. Recent advances in adversarial machine learning (AML) in computer vision have raised interests in recommenders' security.It has been demonstrated that widely adopted model-based recommenders, e.g., BPR-MF, are not r...
Preprint
Full-text available
Visual-based recommender systems (VRSs) enhance recommendation performance by integrating users' feedback with the visual features of items' images. Recently, human-imperceptible image perturbations, defined \textit{adversarial samples}, have been shown capable of altering the VRSs performance, for example, by pushing (promoting) or nuking (demotin...
Preprint
Full-text available
Visually-aware recommender systems (VRSs) enhance the semantics of user-item interactions with visual features extracted from item images when they are available. Traditionally, VRSs leverage the representational power of pretrained convolutional neural networks (CNNs) to perform the item recommendation task. The adoption of CNNs is mainly attribut...
Conference Paper
Full-text available
In the e-Health domain, new and continuously evolving threats emerge every day. The security of e-Health telemonitoring systems is no longer negligible. In this paper, we propose a Cyberattack Detection System (CADS) model that exploits artificial intelligence techniques to detect anomalies without requiring a security analyst, explain the maliciou...
Conference Paper
Full-text available
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 scandals, the users are now worried about sharing their data. In the last decade, Federated Learning has...
Chapter
Full-text available
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 scandals, the users are now worried about sharing their data. In the last decade, Federated Learning has...