Tommaso Di Noia

• Professor
• Professor (Full) at Polytechnic University of Bari

Large Language Models (LLMs) have become increasingly central to recommendation scenarios due to their remarkable natural language understanding and generation capabilities. Although significant research has explored the use of LLMs for various recommendation tasks, little effort has been dedicated to verifying whether they have memorized public re...

We present a systematic study of provider-side data poisoning in retrieval-augmented recommender systems (RAG-based). By modifying only a small fraction of tokens within item descriptions -- for instance, adding emotional keywords or borrowing phrases from semantically related items -- an attacker can significantly promote or demote targeted items....

Conversational agents (CAs), such as chatbots or virtual assistants, represent Artificial Intelligence (AI) systems designed to facilitate human–machine interaction through natural language. These systems are revolutionizing communication by improving efficiency, effectiveness, and user-friendliness. CAs find applications across various domains, in...

This work takes a critical stance on previous studies concerning fairness evaluation in Large Language Model (LLM)-based recommender systems, which have primarily assessed consumer fairness by comparing recommendation lists generated with and without sensitive user attributes. Such approaches implicitly treat discrepancies in recommended items as b...

This demo paper presents AirSense-R, a privacy-preserving mobile application that provides real-time, pollution-aware recommendations for points of interest (POIs) in urban environments. By combining real-time air quality monitoring data with user preferences, the proposed system aims to help users make health-conscious decisions about the location...

This demo paper presents \airtown, a privacy-preserving mobile application that provides real-time, pollution-aware recommendations for points of interest (POIs) in urban environments. By combining real-time Air Quality Index (AQI) data with user preferences, the proposed system aims to help users make health-conscious decisions about the locations...

Recent advancements in diffusion models have significantly broadened the possibilities for editing images of real-world objects. However, performing non-rigid transformations, such as changing the pose of objects or image-based conditioning, remains challenging. Maintaining object identity during these edits is difficult, and current methods often...

This study presents Poison-RAG, a framework for adversarial data poisoning attacks targeting retrieval-augmented generation (RAG)-based recommender systems. Poison-RAG manipulates item metadata, such as tags and descriptions, to influence recommendation outcomes. Using item metadata generated through a large language model (LLM) and embeddings deri...

Multistakeholder recommender systems are those that account for the impacts and preferences of multiple groups of individuals, not just the end users receiving recommendations. Due to their complexity, evaluating these systems cannot be restricted to the overall utility of a single stakeholder, as is often the case of more mainstream recommender sy...

Multistakeholder recommender systems are defined as those that account for ``the preferences of multiple parties when generating recommendations, especially when these parties are on different sides of the recommendation interaction.'' Due to their complexity, evaluating these systems cannot be restricted to the overall utility of a single stakehol...

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 identify suitable pipelines. Several frameworks have been proposed to improve reproducibility, covering the...

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...

In recent years, 3D models have gained popularity in various fields, including entertainment, manufacturing, and simulation. However, manually creating these models can be a time-consuming and resource-intensive process, making it impractical for large-scale industrial applications. To address this issue, researchers are exploiting Artificial Intel...

In specific domains like fashion, music, and movie recommendation, the multi-faceted features characterizing products and services may influence each customer on online selling platforms differently, paving the way to novel multimodal recommendation models that can learn from such multimodal content. According to the literature, the common multimod...

In the realm of music recommendation, sequential recommender systems have shown promise in capturing the dynamic nature of music consumption. Nevertheless, traditional Transformer-based models, such as SASRec and BERT4Rec, while effective, encounter challenges due to the unique characteristics of music listening habits. In fact, existing models str...

The increasing demand for online fashion retail has boosted research in fashion compatibility modeling and item retrieval, focusing on matching user queries (textual descriptions or reference images) with compatible fashion items. A key challenge is top-bottom retrieval, where precise compatibility modeling is essential. Traditional methods, often...

Not at all. In this work, we question this procedure, highlighting that it would further damage the pipeline of any multimodal recom-mender system. First, we show that the lack of (some) modalities is, in fact, a widely-diffused phenomenon in multimodal recommendation. Second, we propose a pipeline that imputes missing multi-modal features in recom...

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 performance. Nevertheless, this investigation still disregards that GNNs treat the recommendation d...

The realm of music composition, augmented by technological advancements such as computers and related equipment, has undergone significant evolution since the 1970s. In the field algorithmic composition, however, the incorporation of artificial intelligence (AI) in sound generation and combination has been limited. Existing approaches predominantly...

Recommender systems (RSs) provide customers with a personalized navigation experience within the vast catalogs of products and services ofered on popular online platforms. Despite the substantial success of traditional RSs, recommendation remains a highly challenging task, especially in speciic scenarios and domains. For example, human ainity for i...

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...

Multimodal recommender systems work by augmenting the representation of the products in the catalogue through multimodal features extracted from images, textual descriptions, or audio tracks characterising such products. Nevertheless, in real-world applications, only a limited percentage of products come with multimodal content to extract meaningfu...

In this work, we introduce Ducho 2.0, the latest stable version of our framework. Differently from Ducho, Ducho 2.0 offers a more personalized user experience with the definition and import of custom extraction models fine-tuned on specific tasks and datasets. Moreover, the new version is capable of extracting and processing features through multim...

This research methodically assesses the fairness of RecLLMs by examining how recommendations might vary with the inclusion of sensitive attributes such as gender, age, and their intersections, through both similarity alignment and true preference alignment. By analyzing recommendations generated under different conditions—including the use of sensi...

This study presents an innovative approach to the application of large language models (LLMs) in clinical decision-making, focusing on OpenAI’s ChatGPT. Our approach introduces the use of contextual prompts-strategically designed to include task description, feature description, and crucially, integration of domain knowledge-for high-quality binary...

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-...

Graph neural networks (GNNs) have gained prominence in recommendation systems in recent years. By representing the user-item matrix as a bipartite and undirected graph, GNNs have demonstrated their potential to capture short-and long-distance user-item interactions, thereby learning more accurate preference patterns than traditional recommendation...

Safety and security issues for Critical Infrastructures are growing as attackers adopt drones as an attack vector flying in sensitive airspaces, such as airports, military bases, city centers, and crowded places. Despite the use of UAVs for logistics, shipping recreation activities, and commercial applications, their usage poses severe concerns to...

Multimodal-aware recommender systems (MRSs) exploit multi-modal content (e.g., product images or descriptions) as items' side information to improve recommendation accuracy. While most of such methods rely on factorization models (e.g., MFBPR) as base architecture, it has been shown that MFBPR may be affected by popularity bias, meaning that it inh...

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...

EEG-based brain-computer interface (BCI) devices have proved to be powerful tools for predicting human emotions. Although Deep learning (DL) techniques have been extensively used to build emotion recognition architectures using EEG-based BCI, they lack interpretability. We propose a prototype of an EEG-based emotion recognition system that can dete...

The textile and apparel industries have grown tremendously over the last few 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...

Although beyond-accuracy metrics have gained attention in the last decade, the accuracy of recommendations is still considered the gold standard to evaluate Recommender Systems (RSs). This approach prioritizes the accuracy of recommendations, neglecting the quality of suggestions to enhance user needs, such as diversity and novelty, as well as trus...

Recommender systems (RSs) provide customers with a personalized navigation experience within the vast catalogs of products and services offered on popular online platforms. Despite the substantial success of traditional RSs, recommendation remains a highly challenging task, especially in specific scenarios and domains. For example, human affinity f...

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 construct more refined user/item profiles for recommendations. However, existing literature lacks a sha...

In recent years, 3D models have gained popularity in various fields, including entertainment, manufacturing, and simulation. However, manually creating these models can be a time-consuming and resource-intensive process, making it impractical for large-scale industrial applications. To address this issue, researchers are exploiting Artificial Intel...

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...

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, graph CF utilizes short- and long-range connections to extract collaborative signals that yield mo...

This study presents an innovative approach to the application of large language models (LLMs) in clinical decision-making, focusing on OpenAI's ChatGPT. Our approach introduces the use of contextual prompts-strategically designed to include task description, feature description, and crucially, integration of domain knowledge-for high-quality binary...

In domains such as fashion, music, food, and micro-video recommendation, items' representation can be suitably enhanced through multimodal side information (extracted from images, texts, or audio). Multimodal-aware recommender systems (MRSs) refer to the family of RSs which integrates extracted multimodal items' features into the classical recommen...

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 baseline papers without verifying their validity for the specific configuration under analysis. Our work ad...

Brain-Computer Interfaces (BCI) allows systems to be controlled by signals derived from Electroencephalogram (EEG) analysis. Several low-cost electroencephalographs are available on the market that provides high-quality EEG signals. A very interesting approach in this domain is to represent a user's mental state by using an EEG signal. In this pape...

Graph convolutional networks (GCNs) are taking over collaborative filtering-based recommendation. Their message-passing schema effectively distills the collaborative signal throughout the user-item graph by propagating informative content from neighbor to ego nodes. In this demonstration, we show how to run complete experimental pipelines with six...

Information Retrieval (IR) and Recommender Systems (RS) tasks are moving from computing a ranking of final results based on a single metric to multi-objective problems. Solving these problems leads to a set of Pareto-optimal solutions, known as Pareto frontier, in which no objective can be further improved without hurting the others. In principle,...

Collaborative filtering models have undoubtedly dominated the scene of recommender systems in recent years. However, due to the little use of content information, they narrowly focus on accuracy, disregarding a higher degree of personalization. In the meanwhile, knowledge graphs are arousing considerable interest in recommendation models thanks to...

To date, graph collaborative filtering (CF) strategies have been shown to outperform pure CF models in generating accurate recommendations. Nevertheless, recent works have raised concerns about fairness and potential biases in the recommendation landscape since unfair recommendations may harm the interests of Consumers and Producers (CP). Acknowled...

[This corrects the article DOI: 10.3389/fnut.2022.811076.].

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...

To date, graph collaborative filtering (CF) strategies have been shown to outperform pure CF models in generating accurate recommendations. Nevertheless, recent works have raised concerns about fairness and potential biases in the recommendation landscape since unfair recommendations may harm the interests of Consumers and Producers (CP). Acknowled...

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...

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...

Purpose Growing awareness of the biological and clinical value of nutrition in frailty settings calls for further efforts to investigate dietary gaps to act sooner to achieve focused management of aging populations. We cross-sectionally examined the eating habits of an older Mediterranean population to profile dietary features most associated with...

Nowadays, modern technology is widespread in sports; therefore, finding an excellent approach to extracting knowledge from data is necessary. Machine Learning (ML) algorithms can be beneficial in biomechanical data management because they can handle a large amount of data. A fencing lunge represents an exciting scenario since it necessitates neurom...

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...

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...

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...

—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...

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...

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...

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...

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...

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...

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...

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...

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...

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...