GreenDB: Toward a Product-by-Product Sustainability Database

Abstract

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.

Automated Extraction of Fine-Grained Standardized Product Information from Unstructured Multilingual Web Data

“…In our experiments, we use two data sets, the GreenDB [6] and the Farfetch data set [9]. The GreenDB 5 is a multilingual data set covering 5 European shops with about 576k unique products of the 37 most important product categories following the GPC taxonomy.…”