Fedelucio Narducci

• PhD
• Assistant Professor at Polytechnic University of Bari

Metabolic dysfunction-associated fatty liver disease (MAFLD) introduces new diagnostic criteria for fatty liver disease that are independent of alcohol consumption and viral hepatitis infection. Therefore, investigating how biochemical and anthropometric factors influence mortality in MAFLD subjects is of significant interest. In this work, we prop...

Cardiovascular disease (CVD) is a general term referring to several heart or blood vessels abnormality. Heart failure (HF), directly associated to (CVD), is a significant global health problem as well as the leading cause of morbidity and mortality. The early detection of this condition is crucial for patient health. Traditional diagnostic methods...

Unit tests represent the most basic level of testing within the software testing lifecycle and are crucial to ensuring software correctness. Designing and creating unit tests is a costly and labor-intensive process that is ripe for automation. Recently, Large Language Models (LLMs) have been applied to various aspects of software development, inclu...

In an era characterized by unprecedented virtual connectivity, paradoxically, individuals often find themselves disconnected from genuine human interactions. The advent of remote working arrangements, compounded by the influence of digital communication platforms, has fostered a sense of isolation among people. Consequently, the prevailing socio-te...

In electroacoustic music composition, particularly in sound synthesis techniques, Deep Learning (DL) provides very effective solutions. However, these architectures generally have a high level of automation and use textual language for human interaction. To improve the relationship between composers and artificial intelligence systems, brain-comput...

Wearable Devices (WDs), encompassing a spectrum from smartwatches to fitness trackers, continuously furnish a wealth of physiological and activity-related data. This trove of information facilitates the creation of robust user models, offering a dynamic lens into users’ daily lives, health patterns, and interaction behaviours. Furthermore, the inte...

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 queries by presenting their questions in natural language. Interactive question answering is a recently proposed and increasingly popular solution that re...

Emotion recognition is crucial in affective computing, aiming to bridge the gap between human emotional states and computer understanding. This study presents NeuroSense, a novel electroencephalography (EEG) dataset utilizing low-cost, sparse electrode devices for emotion exploration. Our dataset comprises EEG signals collected with the portable 4-...

Preference elicitation is a crucial step for every recommendation algorithm. In this paper, we present a strategy that allows users to express their preferences and needs through natural language statements. In particular, our natural language preference elicitation pipeline allows users to express preferences on objective movie features (e.g., act...

Mild Cognitive Impairment (MCI) is a syndrome charac-terized by cognitive impairment that is greater than expected for a subject's age and level of education. Nevertheless, it does not interfere with daily activity. Prevalence in epidemiological and population-based studies ranges from 3% to 19% in adults older than 65 years. A very interesting app...

Brain-computer interfaces are widely used to control machines using Electroencephalography (EEG) signals. Several low-cost electroencephalographs are available on the market that achieves good-quality EEG signals. One of the most intriguing issues for developing biofeedback systems is classifying users' emotional states using EEG signals and Machin...

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 discrimination. Indeed, when sensitive features are omitted (fairness under unawareness), they coul...

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 zero/few-shot prompting conditions. Given such successes, the Recommender Systems (RSs) research comm...

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 discrimination. Indeed, when sensitive features are omitted (fairness under unawareness), they coul...

The increasing application of Artificial Intelligence and Machine Learning models poses potential risks of unfair behaviour and, in light of recent regulations, has attracted the attention of the research community. Several researchers focused on seeking new fairness definitions or developing approaches to identify biased predictions. These approac...

The financial domain is making huge advancements thanks to the exploitation of artificial intelligence. As an example, the credit-worthiness-assessment task is now strongly based on Machine Learning algorithms that make decisions independently from humans. Several studies showed remarkable improvement in reliability, customer care, and return on in...

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 seeking new fairness definitions or developing approaches to identify biased predictions. However, none...

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 discrimination. Indeed, when sensitive features are omitted (fairness under unawareness), they coul...

Metabolic (dysfunction) associated fatty liver disease (MAFLD) establishes new criteria for diagnosing fatty liver disease independent of alcohol consumption and concurrent viral hepatitis infection. However, the long-term outcome of MAFLD subjects is sparse. Few articles are focused on mortality in MAFLD subjects, and none investigate how to predi...

Artificial intelligence (AI) is rapidly becoming the pivotal solution to support critical judgments in many life-changing decisions. In fact, a biased AI tool can be particularly harmful since these systems can contribute to or demote people's well-being. Consequently, government regulations are introducing specific rules to prohibit the use of sen...

—Machine Learning could help the healthcare industry manage huge amounts of data and discover hidden trends and patterns that could help us better understand disease development and treatment. The goal is to define a Neural Network model (NN) to classify physical frailty in aging cohort to identify the frail food and clinical profile. In a 1, 929 o...

Humans engage with other humans and their surroundings through various modalities, most notably speech, sight, and touch. In a conversation, all these inputs provide an overview of how another person is feeling. When translating these modalities to a digital context, most of them are unfortunately lost. The majority of existing conversational recom...

Preference elicitation is a crucial step for every recommendation algorithm. Traditional interaction strategies for eliciting users’ interests and needs range from button-based interfaces, where users have to select what they like among a set of fixed alternatives, to more recent conversational interfaces, where users have to reply to some question...

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...

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...

Conversational Recommender Systems have received widespread attention in both research and practice. They assist people in finding relevant and interesting items through a multi-turn conversation. The use of natural language interaction also allows users to express their preferences with more flexibility. However, these systems often have to work i...

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...

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...

Humans engage with other humans and their surroundings through various modalities, most notably speech, sight, andtouch. In a conversation, all these inputs provide an overview of how another person is feeling. When translating thesemodalities to a digital context, most of them are unfortunately lost. The majority of existing conversational recomme...

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...

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...

In this article, we present MyrrorBot , a personal digital assistant implementing a natural language interface that allows the users to: (i) access online services, such as music, video, news, and food recommendation s, in a personalized way, by exploiting a strategy for implicit user modeling called holistic user profiling ; (ii) query their own u...

Recommender systems have been widely used in the Financial Services domain and can play a crucial role in personal loan comparison platforms. However, the use of AI in this domain has brought to light many opportunities as well as new ethical and legal risks. Customers can trust the suggestions of these systems only if the recommendation process is...

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...

Decision-making systems have been widely used in the Financial Services domain. AI is bringing both many innovations and opportunities as well as new risks linked to ethical considerations. Customer trust is at the forefront of customer retention. To build trust, there is the need to make the decision process Interpretable, Understandable, and Trus...

Business agility requires support from recommendation systems, but explaining recommendations may yield information disclosure. We analyze how to provide explanations in the scenario of Multi-Stakeholder Recommendation where the sensible information of one stakeholder should not be disclosed in the explanation to another stakeholder. Among the seve...

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...

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...

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 crucial concern since users may not be willing to share their sensitive preferences (e.g., visited lo...

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...

Decision making is the cognitive process of identifying and choosing alternatives based on preferences, beliefs, and degree of importance given by the decision maker to objects or actions. For instance, choosing which movie to watch is a simple, small-sized decision-making process. Recommender systems help people to make this kind of choices, usual...

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 crucial concern since users may not be willing to share their sensitive preferences (e.g., visited lo...

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, privacy is a crucial concern since users may not be willing to share their sensitive preferences (e.g., visited locations...

In this article, we present HealthAssistantBot, an intelligent virtual assistant able to talk with patients in order to understand their symptomatology, suggest doctors, and monitor treatments and health parameters. In a simple way, by exploiting a natural language-based interaction, the system allows the user to create her health profile, to descr...

Conversational Recommender Systems (CoRSs) implement a paradigm that allows users to interact in natural language with the system for defining their preferences and discovering items that best fit their needs. CoRSs can be straightforwardly implemented as chatbots that, nowadays, are becoming more and more popular for several applications, such as...

Digital Assistants (DA) such as Amazon Alexa, Siri, or Google Assistant are now gaining great diffusion, since they allow users to execute a wide range of actions through messages in natural language. Even though DAs are able to complete tasks such as sending texts, making phone calls, or playing songs, they do not yet implement recommendation faci...

We live in a time characterized by the continuous and massive production of textual and personal data, shared on Web platforms like Facebook, LinkedIn, Twitter, Wikipedia, and so on. These data often reveal very valuable information for those systems that offer an intelligent and personalized information access, such as personalized search engines,...

The importance of content-based features in intelligent information access systems as search engines, information filtering tools, and recommender systems has been thoroughly discussed in the Introduction of this book. All the examples we have provided showed that textual data can be really useful to: (i) tackle some of the issues that affect data...

In this chapter, we introduce a variety of techniques for endogenous semantics representation of textual content. Such techniques, also defined as distributional semantics methods, are based on the idea that the meaning of a word can be inferred by analyzing its distribution in the context of ordinary and concrete language usage.

In the introduction of this book, we have thoroughly discussed the importance of adaptive and personalized systems in a broad range of applications. In particular, we have motivated the use of content-based information and textual data, and we have analyzed all the possible limitations of approaches based on keyword-based representation. In this ch...

In this chapter, we introduce a different vision of the concept of semantics, since we will present a variety of techniques that allow to build a semantics-aware representation without the need of large corpora of textual data that are mandatory for endogenous semantics representation methodologies.

This monograph gives a complete overview of the techniques and the methods for semantics-aware content representation and shows how to apply such techniques in various use cases, such as recommender systems, user profiling and social media analysis. Throughout the book, the authors provide an extensive analysis of the techniques currently proposed...

Electronic Program Guides (EPGs) are systems that allow users of media applications, such as web TVs, to navigate scheduling information about current and upcoming programming. Personalized EPGs help users to overcome information overload in this domain, by exploiting recommender systems that automatically compile lists of novel and diverse video a...

Chatbots are becoming more and more popular for several applications like customer care, health care, medical diagnoses. Generally, they have an interaction with users based on natural language, buttons, or both. In this paper we study the user interaction with a content-based recommender system implemented as a Telegram chatbot. More specifically,...

More and more precise and powerful recommendation algorithms and techniques have been proposed over the last years able to effectively assess users' tastes and predict information that would probably be of interest for them. Most of these approaches rely on the collaborative paradigm (often exploiting machine learning techniques) and do not take in...

In this article we propose a framework that generates natural language explanations supporting the suggestions generated by a recommendation algorithm. The cornerstone of our approach is the usage of Linked Open Data (LOD) for explanation aims. Indeed, the descriptive properties freely available in the LOD cloud (e.g., the author of a book or the d...

The blue feeling is the sensation which affects people when they feel down, depressed, sad and more generally when they are in a bad feeling state. In some cases, it is a recurring situation in their everyday life and it can be the first symptom of more complex psychological diseases such as depression. In the last decade, as consequence of the qui...

HealthNet (HN) is a social network that brings together patients with similar health conditions. HN helps users in finding a solution to their health problems by suggesting doctors and health facilities that best fit the patient profile. Indeed, the core component of HN is a recommender system that suggests patients similar to the target user and s...

T-RecS is a system which implements several computational linguistic techniques for analyzing word usage variations over time periods in a document collection. We analyzed ACM RecSys conference proceedings from the first edition held in 2007, to the one held in 2015. The idea is to identify linguistic phenomena that reflect some interesting variati...

This paper presents T-RecS (Temporal analysis of Recommender Systems conference proceedings), a framework that supplies services to analyze the Recommender Systems Conference proceedings from the first edition, held in 2007, to the last one, held in 2015, under a temporal point of view. The idea behind T-RecS is to identify linguistic phenomena tha...

In this paper we present ExpLOD, a framework which exploits the information available in the Linked Open Data (LOD) cloud to generate a natural language explanation of the suggestions produced by a recommendation algorithm. The methodology is based on building a graph in which the items liked by a user are connected to the items recommended through...

The growth of the Web is the most influential factor that contributes to the increasing importance of text retrieval and filtering systems. On one hand, the Web is becoming more and more multilingual, and on the other hand users themselves are becoming increasingly polyglot. In this context, platforms for intelligent information access as search en...

Nowadays, repositories of services are becoming increasingly useful in the management of many public and private service provider organizations. In order to make a repository an integrated representation of all services delivered in an organization, a unified representation is desirable. Since several repositories of services, each potentially char...

Emotions play a crucial role in the decision making process. Frequently, choices are strongly influenced by the mood of the moment, and the same person could take different decisions at different time on the same topic. Recommender systems, that are definitively recognized as tools for supporting the decision making process, demonstrated to be more...

This paper describes the participation of the UNIBA team in the Named Entity rEcognition and Linking (NEEL) Challenge. We propose a knowledge-based algorithm able to recognize and link named entities in English tweets. The approach combines the simple Lesk algorithm with information coming from both a distributional semantic model and usage frequen...

In this work we present a semantic recommender system able to suggest doctors and hospitals that best fit a specific patient profile. The recommender system is the core component of the social network named HealthNet (HN). The recommendation algorithm first computes similarities among patients, and then generates a ranked list of doctors and hospit...

Content-based recommender systems (CBRSs) rely on item and user descriptions (content) to build item representations and user profiles that can be effectively exploited to suggest items similar to those a target user already liked in the past. Most content-based recommender systems use textual features to represent items and user profiles, hence th...

This paper provides an overview of the work done in the ESWC Linked Open Data-enabled Recommender Systems challenge, in which we proposed an ensemble of algorithms based on popularity, Vector Space Model, Random Forests, Logistic Regression, and PageRank, running on a diverse set of semantic features. We ranked 1st in the top-N recommendation task,...

CroSer (Cross-language Semantic Retrieval) is an ir system able to discover links between e-gov services described in different languages. CroSeR supports public administrators to link their own source catalogs of e-gov services described in any language to a target catalog whose services are described in English and are available in the Linked Ope...

This paper7 presents the preliminary results of a joint research project about Smart Cities. This project is adopting a multi-disciplinary approach that combines artificial intelligence techniques with psychology research to monitor the current state of the city of L'Aquila after the dreadful earthquake of April 2009. This work focuses on the descr...

Public administrations are aware of the advantages of sharing Open Government Data in terms of transparency, development of improved services, collaboration between stakeholders, and spurring new economic activities. Initiatives for the publication and interlinking of government service catalogs as Linked Open Data (lod) support the interoperabilit...

The recent explosion of Big Data is offering new chances and challenges to all those platforms that provide personalized access to information sources, such as recommender systems and personalized search engines. In this context, social networks are gaining more and more interests since they represent a perfect source to trigger personalization tas...

The main contribution of this work is the comparison of different techniques for representing user preferences extracted by analyzing data gathered from social networks, with the aim of constructing more transparent (human-readable) and serendipitous user profiles. We compared two different user models representations: one based on keywords and one...

The rapid growth of the so-called Web 2.0 has changed the surfers’ behavior. A new democratic vision emerged, in which users can actively contribute to the evolution of the Web by producing new content or enriching the existing one with user generated metadata. In this context the use of tags, keywords freely chosen by users for describing and orga...