Giovannipaolo Ferrari
University of Salerno
Summary
Giovannipaolo Ferrari holds dual PhDs: one in Sociology and Social Research from the University of Salerno, Italy, and another in Applied Linguistics from the Université Paul Valéry Montpellier 3, France, where he was also conferred the title of Doctor Europaeus. Currently, he serves as an Assistant Professor at the Department of Human, Philosophical, and Educational Sciences (DISUFF) at the University of Salerno, where he teaches courses in sociology, including the sociology of sport and well-being, sociology of health, as well as research methods (both qualitative and quantitative), data analysis, and digital methods.
In addition to his academic duties, Dr. Ferrari is the co-director of the DiSoSW Social Research Lab (Digital Society, Sport & Well-being Social Research Lab) at the University of Salerno. He also holds the position of Associate Researcher at CeRIES (Center for Research “Individuals, Experiences, Societies”) at the University of Lille, France. His extensive teaching experience includes roles at the Université Paul Valéry Montpellier in France and at institutions in China, such as Dalian University of Foreign Languages and the University of Nottingham’s Ningbo campus. He has also been a visiting scholar at Middlebury College in Vermont, USA.
Dr. Ferrari’s research interests are centered on digital methods for social research, public sociology, and the sociology of sport and well-being.
Edited Journals
IECE Contributions
Review Article
| 15 October 2024
Recommender System: A Comprehensive Overview of Technical Challenges and Social Implications
Abstract
The proliferation of Recommender Systems (RecSys), driven by their expanding application domains, explosive data growth, and exponential advancements in computing capabilities, has cultivated a dynamic and evolving research landscape. This paper comprehensively reviews the foundational concepts, methodologies, and challenges associated with RecSys from technological and social scientific lenses. Initially, it categorizes personalized RecSys technical solutions into five paradigms: collaborative filtering, scenario-aware, knowledge & data co-driven approaches, large language models, and hybrid models integrating diverse data sources. Subsequently, the paper analyses the key challenges and fut...
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