GreenRec: A Large-Scale Dataset for Green Food Recommendation

Lingzi Zhang, Yinan Zhang, Xin Zhou, Zhiqi Shen

Abstract: In response to growing interest in sustainable living from both governmental and public spheres, there is an increased effort to understand environmental implications. Recommendation systems, which are widely applied in various aspects of daily life, are crucial tools in encouraging and guiding users toward sustainable choices. However, existing public recommendation datasets primarily focus on user-item interactions and lack sufficient emphasis on sustainability, posing significant challenges to developing recommendations for sustainable items. In this work, we enrich a public food recommendation dataset by assigning environmental impact, nutritional impact, and health scores to each recipe, following well-recognized sustainability measurements. Through this work, we aim to lay a groundwork for recommending foods that are both healthy and environmentally conscious, all while maintaining recommendation accuracy.