Search Results for author: Tommaso Di Noia

Found 40 papers, 17 papers with code

CFaiRLLM: Consumer Fairness Evaluation in Large-Language Model Recommender System

no code implementations8 Mar 2024 Yashar Deldjoo, Tommaso Di Noia

In the evolving landscape of recommender systems, the integration of Large Language Models (LLMs) such as ChatGPT marks a new era, introducing the concept of Recommendation via LLM (RecLLM).

Attribute Fairness +3

Formalizing Multimedia Recommendation through Multimodal Deep Learning

1 code implementation11 Sep 2023 Daniele Malitesta, Giandomenico Cornacchia, Claudio Pomo, Felice Antonio Merra, Tommaso Di Noia, Eugenio Di Sciascio

Recommender systems (RSs) offer personalized navigation experiences on online platforms, but recommendation remains a challenging task, particularly in specific scenarios and domains.

Benchmarking Multimedia recommendation +1

Evaluating ChatGPT as a Recommender System: A Rigorous Approach

1 code implementation7 Sep 2023 Dario Di Palma, Giovanni Maria Biancofiore, Vito Walter Anelli, Fedelucio Narducci, Tommaso Di Noia, Eugenio Di Sciascio

Through thoroughly exploring ChatGPT's abilities in recommender systems, our study aims to contribute to the growing body of research on the versatility and potential applications of large language models.

Large Language Model Recommendation Systems

On Popularity Bias of Multimodal-aware Recommender Systems: a Modalities-driven Analysis

1 code implementation24 Aug 2023 Daniele Malitesta, Giandomenico Cornacchia, Claudio Pomo, Tommaso Di Noia

Multimodal-aware recommender systems (MRSs) exploit multimodal content (e. g., product images or descriptions) as items' side information to improve recommendation accuracy.

Recommendation Systems

A Topology-aware Analysis of Graph Collaborative Filtering

1 code implementation21 Aug 2023 Daniele Malitesta, Claudio Pomo, Vito Walter Anelli, Alberto Carlo Maria Mancino, Eugenio Di Sciascio, Tommaso Di Noia

The successful integration of graph neural networks into recommender systems (RSs) has led to a novel paradigm in collaborative filtering (CF), graph collaborative filtering (graph CF).

Collaborative Filtering Graph Sampling +1

ChatGPT-HealthPrompt. Harnessing the Power of XAI in Prompt-Based Healthcare Decision Support using ChatGPT

1 code implementation17 Aug 2023 Fatemeh Nazary, Yashar Deldjoo, Tommaso Di Noia

In essence, this paper bridges the gap between AI and healthcare, proposing a novel methodology for LLMs application in clinical decision support systems.

Binary Classification Decision Making +2

Challenging the Myth of Graph Collaborative Filtering: a Reasoned and Reproducibility-driven Analysis

1 code implementation1 Aug 2023 Vito Walter Anelli, Daniele Malitesta, Claudio Pomo, Alejandro Bellogín, Tommaso Di Noia, Eugenio Di Sciascio

The success of graph neural network-based models (GNNs) has significantly advanced recommender systems by effectively modeling users and items as a bipartite, undirected graph.

Collaborative Filtering Recommendation Systems

Ducho: A Unified Framework for the Extraction of Multimodal Features in Recommendation

1 code implementation29 Jun 2023 Daniele Malitesta, Giuseppe Gassi, Claudio Pomo, Tommaso Di Noia

Motivated by the outlined aspects, we propose \framework, a unified framework for the extraction of multimodal features in recommendation.

Multimodal Recommendation

Interactive Question Answering Systems: Literature Review

no code implementations4 Sep 2022 Giovanni Maria Biancofiore, Yashar Deldjoo, Tommaso Di Noia, Eugenio Di Sciascio, Fedelucio Narducci

Interactive question answering is a recently proposed and increasingly popular solution that resides at the intersection of question answering and dialogue systems.

Question Answering

Conversational Recommendation: Theoretical Model and Complexity Analysis

no code implementations10 Nov 2021 Tommaso Di Noia, Francesco Donini, Dietmar Jannach, Fedelucio Narducci, Claudio Pomo

With this work, we complement empirical research with a theoretical, domain-independent model of conversational recommendation.

Recommendation Systems

Understanding the Effects of Adversarial Personalized Ranking Optimization Method on Recommendation Quality

no code implementations29 Jul 2021 Vito Walter Anelli, Yashar Deldjoo, Tommaso Di Noia, Felice Antonio Merra

However, a key overlooked aspect has been the beyond-accuracy performance of APR, i. e., novelty, coverage, and amplification of popularity bias, considering that recent results suggest that BPR, the building block of APR, is sensitive to the intensification of biases and reduction of recommendation novelty.

Learning-To-Rank Recommendation Systems

Sparse Feature Factorization for Recommender Systems with Knowledge Graphs

no code implementations29 Jul 2021 Vito Walter Anelli, Tommaso Di Noia, Eugenio Di Sciascio, Antonio Ferrara, Alberto Carlo Maria Mancino

In fact, in these cases we have that with a large number of high-quality features, the resulting models are more complex and difficult to train.

Collaborative Filtering Knowledge Graphs +1

Reenvisioning Collaborative Filtering vs Matrix Factorization

1 code implementation28 Jul 2021 Vito Walter Anelli, Alejandro Bellogín, Tommaso Di Noia, Claudio Pomo

We replicate experiments from three papers that compare Neural Collaborative Filtering (NCF) and Matrix Factorization (MF), to extend the analysis to other evaluation dimensions.

Collaborative Filtering

Multi-Step Adversarial Perturbations on Recommender Systems Embeddings

no code implementations3 Oct 2020 Vito Walter Anelli, Alejandro Bellogín, Yashar Deldjoo, Tommaso Di Noia, Felice Antonio Merra

However, while the single-step fast gradient sign method (FGSM) is the most explored perturbation strategy, multi-step (iterative) perturbation strategies, that demonstrated higher efficacy in the computer vision domain, have been highly under-researched in recommendation tasks.

Recommendation Systems

An Empirical Study of DNNs Robustification Inefficacy in Protecting Visual Recommenders

no code implementations2 Oct 2020 Vito Walter Anelli, Tommaso Di Noia, Daniele Malitesta, Felice Antonio Merra

However, since adversarial training techniques have proven to successfully robustify DNNs in preserving classification accuracy, to the best of our knowledge, two important questions have not been investigated yet: 1) How well can these defensive mechanisms protect the VRSs performance?

Recommendation Systems

How to Put Users in Control of their Data in Federated Top-N Recommendation with Learning to Rank

no code implementations17 Aug 2020 Vito Walter Anelli, Yashar Deldjoo, Tommaso Di Noia, Antonio Ferrara, Fedelucio Narducci

Recommendation services are extensively adopted in several user-centered applications as a tool to alleviate the information overload problem and help users in orienteering in a vast space of possible choices.

Federated Learning Learning-To-Rank +1

Prioritized Multi-Criteria Federated Learning

no code implementations17 Jul 2020 Vito Walter Anelli, Yashar Deldjoo, Tommaso Di Noia, Antonio Ferrara

In order to address these issues, Federated Learning (FL) has been recently proposed as a means to build ML models based on private datasets distributed over a large number of clients, while preventing data leakage.

Federated Learning Image Classification +1

A survey on Adversarial Recommender Systems: from Attack/Defense strategies to Generative Adversarial Networks

1 code implementation20 May 2020 Yashar Deldjoo, Tommaso Di Noia, Felice Antonio Merra

Latent-factor models (LFM) based on collaborative filtering (CF), such as matrix factorization (MF) and deep CF methods, are widely used in modern recommender systems (RS) due to their excellent performance and recommendation accuracy.

BIG-bench Machine Learning Collaborative Filtering +1

How to make latent factors interpretable by feeding Factorization machines with knowledge graphs

1 code implementation11 Sep 2019 Vito Walter Anelli, Tommaso Di Noia, Eugenio Di Sciascio, Azzurra Ragone, Joseph Trotta

By relying on the information encoded in the original knowledge graph, we have also evaluated the semantic accuracy and robustness for the knowledge-aware interpretability of the final model.

Informativeness Knowledge Graphs +1

Assessing the Impact of a User-Item Collaborative Attack on Class of Users

no code implementations21 Aug 2019 Yashar Deldjoo, Tommaso Di Noia, Felice Antonio Merra

While previous works have focused on evaluating shilling attack strategies from a global perspective paying particular attention to the effect of the size of attacks and attacker's knowledge, in this work we explore the effectiveness of shilling attacks under novel aspects.

Collaborative Filtering Recommendation Systems

Towards Effective Device-Aware Federated Learning

no code implementations20 Aug 2019 Vito Walter Anelli, Yashar Deldjoo, Tommaso Di Noia, Antonio Ferrara

With the wealth of information produced by social networks, smartphones, medical or financial applications, speculations have been raised about the sensitivity of such data in terms of users' personal privacy and data security.

Federated Learning Information Retrieval +1

Recommender Systems Fairness Evaluation via Generalized Cross Entropy

no code implementations19 Aug 2019 Yashar Deldjoo, Vito Walter Anelli, Hamed Zamani, Alejandro Bellogin, Tommaso Di Noia

We present a probabilistic framework based on generalized cross entropy to evaluate fairness of recommender systems under this perspective, where we show that the proposed framework is flexible and explanatory by allowing to incorporate domain knowledge (through an ideal fair distribution) that can help to understand which item or user aspects a recommendation algorithm is over- or under-representing.

Fairness Recommendation Systems

Knowledge-aware Autoencoders for Explainable Recommender Sytems

no code implementations17 Jul 2018 Vito Bellini, Angelo Schiavone, Tommaso Di Noia, Azzurra Ragone, Eugenio Di Sciascio

Recommender Systems have been widely used to help users in finding what they are looking for thus tackling the information overload problem.

Recommendation Systems

Computing recommendations via a Knowledge Graph-aware Autoencoder

1 code implementation13 Jul 2018 Vito Bellini, Angelo Schiavone, Tommaso Di Noia, Azzurra Ragone, Eugenio Di Sciascio

In the last years, deep learning has shown to be a game-changing technology in artificial intelligence thanks to the numerous successes it reached in diverse application fields.

The importance of being dissimilar in Recommendation

1 code implementation11 Jul 2018 Vito Walter Anelli, Joseph Trotta, Tommaso Di Noia, Eugenio Di Sciascio, Azzurra Ragone

Similarity measures play a fundamental role in memory-based nearest neighbors approaches.

Auto-Encoding User Ratings via Knowledge Graphs in Recommendation Scenarios

no code implementations24 Jun 2017 Vito Bellini, Vito Walter Anelli, Tommaso Di Noia, Eugenio Di Sciascio

In the last decade, driven also by the availability of an unprecedented computational power and storage capabilities in cloud environments we assisted to the proliferation of new algorithms, methods, and approaches in two areas of artificial intelligence: knowledge representation and machine learning.

Knowledge Graphs Recommendation Systems

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