Vito Walter Anelli

• Polytechnic University of Bari

Inductive link prediction is emerging as a key paradigm for real-world knowledge graphs (KGs), where new entities frequently appear and models must generalize to them without retraining. Predicting links in a KG faces the challenge of guessing previously unseen entities by leveraging generalizable node features such as subgraph structure, type anno...

In the realm of music recommendation, sequential recommenders have shown promise in capturing the dynamic nature of music consumption. A key characteristic of this domain is repetitive listening, where users frequently replay familiar tracks. To capture these repetition patterns, recent research has introduced Personalised Popularity Scores (PPS),...

Factual hallucinations are a major challenge for Large Language Models (LLMs). They undermine reliability and user trust by generating inaccurate or fabricated content. Recent studies suggest that when generating false statements, the internal states of LLMs encode information about truthfulness. However, these studies often rely on synthetic datas...

Large Language Models (LLMs) have rapidly become central to NLP, demonstrating their ability to adapt to various tasks through prompting techniques, including sentiment analysis. However, we still have a limited understanding of how these models capture sentiment-related information. This study probes the hidden layers of Llama models to pinpoint w...

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...

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 chapter briefly synthesizes the key themes discussed throughout this work and outlines a roadmap for future research directions in this critical field.

This chapter outlines potential privacy and security risks inherent in IRRSs. It explores how personal data and models can be protected and discusses relevant regulations and corresponding technical solutions. The chapter closes by discussing open challenges related to privacy and security in IRRSs and points to additional related work.

This chapter discusses recent regulatory and ethical aspects of transparency, clarifies the relevant terminology, and highlights the benefits, challenges, and barriers to transparency in IRRSs. It then discusses techniques for enhancing transparency in IRRSs, outlines how transparency can be evaluated and achieved via documentation, and presents ho...

This chapter reviews existing regulations on discrimination, provides a multifaceted categorization of biases and fairness criteria, discusses techniques to measure popularity and demographic biases, presents methods to mitigate harmful biases, and concludes with a reflection on open challenges.

This chapter investigates regulatory activities and policies in the European Union, the USA, and—considering its highly different cultural and political background—China, where regulation of AI and IRRSs has quite different aims than in the Western world.

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 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...

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...

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...

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...

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...

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...

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...

Data science has been the foundation of recommender systems for a long time. Over the past few decades, various recommender systems have been developed using different data science and machine learning methodologies and techniques. However, no existing work systematically discusses the significant relationships between data science and recommender...

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,...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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 (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...

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...

After the success the RecSys 2020 Challenge, we are describing a novel and bigger dataset that was released in conjunction with the ACM RecSys Challenge 2021. This year's dataset is not only bigger (~ 1B data points, a 5 fold increase), but for the first time it take into consideration fairness aspects of the challenge. Unlike many static datsets,...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...