# GreenDB: Toward a Product-by-Product Sustainability Database

arXiv: https://arxiv.org/abs/2205.02908 · HTML: https://mlsanity.com/paper/2205.02908/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 18 references that are in the map.
- First posted on arXiv 5 May 2022.

## Builds on (most cited first, 1 of 1)

- [Products-10K: A Large-scale Product Recognition Dataset](https://mlsanity.com/paper/2008.10545/) (2020)

## Built on by (most cited first, 2 of 2)

- [GreenDB -- A Dataset and Benchmark for Extraction of Sustainability Information of Consumer Goods](https://mlsanity.com/paper/2207.10733/) (2022)
- [Automated Extraction of Fine-Grained Standardized Product Information from Unstructured Multilingual Web Data](https://mlsanity.com/paper/2302.12139/) (2023)

## Abstract (arXiv)

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.