Tommaso Di Noia

Semantic-based Approach to Task Assignment of Individual Profiles

2020 · Zenodo (CERN European Organization for Nuclear Research)

Abstract: This paper is focused on the problem of skill matching in an organizational context. We endow the classical weighted bipartite graph approach with a semantic based assignment of arcs weight and we describe a …

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

2019 · arXiv (Cornell University)

Collaborative Filtering (CF) models lie at the core of most recommendation systems due to their state-of-the-art accuracy. They are commonly adopted in e-commerce and online services for their impact on sales volume and/or diversity, and …

How to put users in control of their data in federated top-N recommendation with learning to rank

2021

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 …

Interactive Question Answering Systems: Literature Review

2022 · arXiv (Cornell University)

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 …

Graph Neural Networks for Recommendation: Reproducibility, Graph Topology, and Node Representation

2023 · arXiv (Cornell University)

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 …

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

2024 · arXiv (Cornell University)

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 …

Do LLMs Memorize Recommendation Datasets? A Preliminary Study on MovieLens-1M

2025

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 …

Sound and Music Recommendation with Knowledge Graphs

2016 · ACM Transactions on Intelligent Systems and Technology

The Web has moved, slowly but steadily, from a collection of documents towards a collection of structured data. Knowledge graphs have then emerged as a way of representing the knowledge encoded in such data as …

Elliot: A Comprehensive and Rigorous Framework for Reproducible Recommender Systems Evaluation

2021

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, …