8 papers
Joint Effects of Recommender Systems and Network Structure on the Visibility of Content and Creators
Virginia Morini, Valentina Pansanella, Luca Pappalardo +2
Social media algorithms allocate users' visibility by ranking content within their social networks. Yet, how recommendation logic and network structure jointly shape visibility acr…
Learning by Surprise: Adaptive Mitigation of Model Collapse in Large Language Models
Daniele Gambetta, Gizem Gezici, Fosca Giannotti +3
As AI-generated content increasingly populates the web, generative AI models are at growing risk of being trained on their own outputs, a process known as AI autophagy. This feedba…
Low-Vocality Engagement Shapes Online Participation
Veronica Mesina, Andrea Failla, Luca Pappalardo +1
Online participation is often measured through visible expression, especially posting, yet many consequential forms of engagement occur through less vocal actions such as liking an…
Comparing Explanations is Not Enough, Explain the Change: New Standards are Needed to Explain Behavioral Shifts in Large Language Models
Martino Ciaperoni, Marzio Di Vece, Roberto Pellungrini +3
Large-scale foundation models exhibit \emph{behavioral shifts} when subjected to interventions such as scaling, fine-tuning, reinforcement learning with human feedback, or in-conte…
The Diversity Paradox revisited: Systemic Effects of Feedback Loops in Recommender Systems
Gabriele Barlacchi, Margherita Lalli, Emanuele Ferragina +3
Recommender systems shape individual choices through feedback loops in which user behavior and algorithmic recommendations coevolve over time. The systemic effects of these loops r…
A survey on the impacts of recommender systems on users, items, and human-AI ecosystems
Luca Pappalardo, Salvatore Citraro, Giuliano Cornacchia +12
Recommendation systems and assistants (in short, recommenders) influence through online platforms most actions of our daily lives, suggesting items or providing solutions based on…