  The production, shipping, usage, and disposal of consumer goods have a
substantial impact on greenhouse gas emissions and the depletion of resources.
Modern retail platforms rely heavily on Machine Learning (ML) for their search
and recommender systems. Thus, ML can potentially support efforts towards more
sustainable consumption patterns, for example, by accounting for sustainability
aspects in product search or recommendations. However, leveraging ML potential
for reaching sustainability goals requires data on sustainability.
Unfortunately, no open and publicly available database integrates
sustainability information on a product-by-product basis. In this work, we
present the GreenDB, which fills this gap. Based on search logs of millions of
users, we prioritize which products users care about most. The GreenDB schema
extends the well-known schema.org Product definition and can be readily
integrated into existing product catalogs to improve sustainability information
available for search and recommendation experiences. We present our proof of
concept implementation of a scraping system that creates the GreenDB dataset.

Machine Learning