GreenDB -- A Dataset and Benchmark for Extraction of Sustainability Information of Consumer Goods

• URL: http://arxiv.org/abs/2207.10733v3
• Date: Tue, 16 Aug 2022 16:46:42 GMT
• Title: GreenDB -- A Dataset and Benchmark for Extraction of Sustainability Information of Consumer Goods
• Authors: Sebastian J\"ager, Alexander Flick, Jessica Adriana Sanchez Garcia, Kaspar von den Driesch, Karl Brendel, Felix Biessmann
• Abstract summary: We present GreenDB, a database that collects products from European online shops on a weekly basis. As proxy for the products' sustainability, it relies on sustainability labels, which are evaluated by experts. We present initial results demonstrating that ML models trained with our data can reliably predict the sustainability label of products.
• Score: 58.31888171187044
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: The production, shipping, usage, and disposal of consumer goods have a substantial impact on greenhouse gas emissions and the depletion of resources. Machine Learning (ML) can help to foster sustainable consumption patterns by accounting for sustainability aspects in product search or recommendations of modern retail platforms. However, the lack of large high quality publicly available product data with trustworthy sustainability information impedes the development of ML technology that can help to reach our sustainability goals. Here we present GreenDB, a database that collects products from European online shops on a weekly basis. As proxy for the products' sustainability, it relies on sustainability labels, which are evaluated by experts. The GreenDB schema extends the well-known schema.org Product definition and can be readily integrated into existing product catalogs. We present initial results demonstrating that ML models trained with our data can reliably (F1 score 96%) predict the sustainability label of products. These contributions can help to complement existing e-commerce experiences and ultimately encourage users to more sustainable consumption patterns.

Related papers

• MIND: Multimodal Shopping Intention Distillation from Large Vision-language Models for E-commerce Purchase Understanding [67.26334044239161]
  MIND is a framework that infers purchase intentions from multimodal product metadata and prioritizes human-centric ones. Using Amazon Review data, we create a multimodal intention knowledge base, which contains 1,264,441 million intentions. Our obtained intentions significantly enhance large language models in two intention comprehension tasks.
  arXiv  Detail & Related papers  (2024-06-15T17:56:09Z)
• System Support for Environmentally Sustainable Computing in Data Centers [4.774769264608661]
  Modern data centers suffer from a growing carbon footprint due to insufficient support for environmental sustainability. We present our preliminary results and recognize this as an ongoing initiative with significant potential to advance environmentally sustainable computing in data centers.
  arXiv  Detail & Related papers  (2024-03-19T12:56:02Z)
• Efficiency Pentathlon: A Standardized Arena for Efficiency Evaluation [82.85015548989223]
  Pentathlon is a benchmark for holistic and realistic evaluation of model efficiency. Pentathlon focuses on inference, which accounts for a majority of the compute in a model's lifecycle. It incorporates a suite of metrics that target different aspects of efficiency, including latency, throughput, memory overhead, and energy consumption.
  arXiv  Detail & Related papers  (2023-07-19T01:05:33Z)
• GreenDB: Toward a Product-by-Product Sustainability Database [2.9971739294416717]
  Modern retail platforms rely heavily on Machine Learning (ML) for their search and recommender systems. No open and publicly available database integrates sustainability information on a product-by-product basis. We present our proof of concept implementation of a scraping system that creates the GreenDB dataset.
  arXiv  Detail & Related papers  (2022-05-05T20:24:16Z)
• Towards Revenue Maximization with Popular and Profitable Products [69.21810902381009]
  A common goal for companies marketing is to maximize the return revenue/profit by utilizing the various effective marketing strategies. Finding credible and reliable information on products' profitability is difficult since most products tends to peak at certain times. This paper proposes a general profit-oriented framework to address the problem of revenue based on economic behavior, and conducting the 0n-shelf Popular and most Profitable Products (OPPPs) for the targeted marketing.
  arXiv  Detail & Related papers  (2022-02-26T02:07:25Z)
• Can Online Customer Reviews Help Design More Sustainable Products? A Preliminary Study on Amazon Climate Pledge Friendly Products [0.0]
  This paper investigates and analyzes Amazon product reviews to bring new light on the following question: What sustainable design insights can be identified or interpreted from online product reviews?'' The top 100 reviews, evenly distributed by star ratings, for three product categories are collected, manually annotated, analyzed and interpreted. Between 12% and 20% of the reviews mentioned directly or indirectly aspects or attributes that could be exploited to improve the design of these products from a sustainability perspective.
  arXiv  Detail & Related papers  (2021-12-20T08:57:37Z)
• Can Machine Learning Tools Support the Identification of Sustainable Design Leads From Product Reviews? Opportunities and Challenges [0.0]
  This paper aims to develop an integrated machine learning solution to obtain sustainable design insights from online product reviews automatically. The opportunities and challenges offered by existing frameworks are discussed, illustrated, and positioned along an ad hoc machine learning process.
  arXiv  Detail & Related papers  (2021-12-17T08:53:58Z)
• Multi-Objective Optimization for Value-Sensitive and Sustainable Basket Recommendations [0.0]
  Sustainable consumption aims to minimize the environmental and societal impact of the use of services and products. This article focuses on value-sensitive design of recommender systems, which enable consumers to improve the sustainability of their purchases while respecting their personal values.
  arXiv  Detail & Related papers  (2021-11-10T21:00:40Z)
• SustainBench: Benchmarks for Monitoring the Sustainable Development Goals with Machine Learning [63.192289553021816]
  Progress toward the United Nations Sustainable Development Goals has been hindered by a lack of data on key environmental and socioeconomic indicators. Recent advances in machine learning have made it possible to utilize abundant, frequently-updated, and globally available data, such as from satellites or social media. In this paper, we introduce SustainBench, a collection of 15 benchmark tasks across 7 SDGs.
  arXiv  Detail & Related papers  (2021-11-08T18:59:04Z)
• Analyzing Sustainability Reports Using Natural Language Processing [68.8204255655161]
  In recent years, companies have increasingly been aiming to both mitigate their environmental impact and adapt to the changing climate context. This is reported via increasingly exhaustive reports, which cover many types of climate risks and exposures under the umbrella of Environmental, Social, and Governance (ESG) We present this tool and the methodology that we used to develop it in the present article.
  arXiv  Detail & Related papers  (2020-11-03T21:22:42Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.