Training Green and Sustainable Recommendation Models: Introducing Carbon Footprint Data into Early Stopping Criteria
Year: 2025
Type: conference-paper
Abstract: With the growing focus on Green AI, there is an urgent need for algorithms that are designed to minimize their environmental impact while maintaining satisfying performance.In this paper, we introduce a novel early stopping strategy that considers carbon footprint data while training a recommendation algorithm.In particular, during the training phase, our criterion epoch-by-epoch analyzes the improvement in terms of predictive accuracy and compares it to the increase in carbon emissions.Then, we analyze the trade-off between the scores, and when the accuracy improves at a rate that is not favorable, the training is stopped.In the experimental evaluation, we showed that our strategy could significantly reduce the carbon footprint of several state-ofthe-art recommendation models, with a limited decrease in accuracy and fairness.While more work is needed to automatically balance the trade-off between accuracy and emissions, this paper sheds light on the need for more sustainable recommendation models and takes a significant step toward designing green training strategies.
Authors: Giuseppe Spillo, Allegra De Filippo, Emanuele Fontana, Michela Milano, Giovanni Semeraro
Institutions: University of Bari Aldo Moro, University of Bologna
Language: English
Topic: Green IT and Sustainability
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W4411278043