Lean and Green: Computational Efficiency and Nudging for Sustainable Fashion Recommendation

Sustainability is a complex topic, and it needs consideration in all aspects of recommendations. For example, the fashion industry’s significant environmental and social impact necessitates new approaches to promote sustainable consumption behaviors. This work focuses specifically on two critical and interconnected aspects of sustainable recommendation: promoting environmentally conscious user behavior and reducing the computational environmental footprint of recommendation algorithms. First, it investigates the application of digital nudging techniques in fashion recommender systems to encourage environmentally conscious purchasing decisions. It describes a comprehensive user study with 251 participants, testing three interface designs that incorporate textual nudges and visual sustainability labels. Results demonstrate remarkable success in promoting second-hand garment selection, with 50-67% of participants choosing used items compared to a 7% market rate. However, the computational requirements of multimodal recommendation systems introduce their own environmental considerations. To address this paradox, this work describes a Transformer-based architecture leveraging attention bottlenecks for more efficient multimodal fusion. Early experiments suggest a significant reduction in representation dimensionality while maintaining recommendation performance. The main goal of this work is to provide a complete picture of what sustainability in recommendation entails, from consumer decisions to resource usage by recommendation algorithms.