Training Green and Sustainable Recommendation Models: Introducing Carbon Footprint Data into Early Stopping Criteria

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-of-the-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.