Multi-Objective Optimization for Value-Sensitive and Sustainable Basket Recommendations

• URL: http://arxiv.org/abs/2111.05944v1
• Date: Wed, 10 Nov 2021 21:00:40 GMT
• Title: Multi-Objective Optimization for Value-Sensitive and Sustainable Basket Recommendations
• Authors: Thomas Asikis
• Abstract summary: 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.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Sustainable consumption aims to minimize the environmental and societal impact of the use of services and products. Over-consumption of services and products leads to potential natural resource exhaustion and societal inequalities, as access to goods and services becomes more challenging. In everyday life, a person can simply achieve more sustainable purchases by drastically changing their lifestyle choices and potentially going against their personal values or wishes. Conversely, achieving sustainable consumption while accounting for personal values is a more complex task, as potential trade-offs arise when trying to satisfy environmental and personal goals. 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. Value-sensitive recommendations for sustainable consumption are formalized as a multi-objective optimization problem, where each objective represents different sustainability goals and personal values. Novel and existing multi-objective algorithms calculate solutions to this problem. The solutions are proposed as personalized sustainable basket recommendations to consumers. These recommendations are evaluated on a synthetic dataset, which comprises three established real-world datasets from relevant scientific and organizational reports. The synthetic dataset contains quantitative data on product prices, nutritional values and environmental impact metrics, such as greenhouse gas emissions and water footprint. The recommended baskets are highly similar to consumer purchased baskets and aligned with both sustainability goals and personal values relevant to health, expenditure and taste. Even when consumers would accept only a fraction of recommendations, a considerable reduction of environmental impact is observed.

Related papers

• Best Practices and Lessons Learned on Synthetic Data [83.63271573197026]
  The success of AI models relies on the availability of large, diverse, and high-quality datasets. Synthetic data has emerged as a promising solution by generating artificial data that mimics real-world patterns.
  arXiv  Detail & Related papers  (2024-04-11T06:34:17Z)
• GreenDB -- A Dataset and Benchmark for Extraction of Sustainability Information of Consumer Goods [58.31888171187044]
  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.
  arXiv  Detail & Related papers  (2022-07-21T19:59:42Z)
• Sustaining Security and Safety in ICT: A Quest for Terminology, Objectives, and Limits [0.0]
  Security and safety are intertwined concepts in the world of computing. In recent years, the terms "sustainable security" and "sustainable safety" came into fashion. This paper dissects the idea of sustainable safety and security.
  arXiv  Detail & Related papers  (2022-06-01T07:46:17Z)
• A Simulation Environment and Reinforcement Learning Method for Waste Reduction [50.545552995521774]
  We study the problem of restocking a grocery store's inventory with perishable items over time, from a distributional point of view. The objective is to maximize sales while minimizing waste, with uncertainty about the actual consumption by costumers. We frame inventory restocking as a new reinforcement learning task that exhibits behavior conditioned on the agent's actions.
  arXiv  Detail & Related papers  (2022-05-30T22:48:57Z)
• 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)
• Functional Model of Residential Consumption Elasticity under Dynamic Tariffs [0.0]
  One of the major barriers for the retailers is to understand the consumption elasticity they can expect from their contracted demand response (DR) clients. The elasticity of consumers demand behavior varies from individual to individual. This work proposes a functional model for the consumption elasticity of the DR contracted consumers.
  arXiv  Detail & Related papers  (2021-11-22T15:29:50Z)
• Dual Side Deep Context-aware Modulation for Social Recommendation [50.59008227281762]
  We propose a novel graph neural network to model the social relation and collaborative relation. On top of high-order relations, a dual side deep context-aware modulation is introduced to capture the friends' information and item attraction.
  arXiv  Detail & Related papers  (2021-03-16T11:08:30Z)
• Fairness, Welfare, and Equity in Personalized Pricing [88.9134799076718]
  We study the interplay of fairness, welfare, and equity considerations in personalized pricing based on customer features. We show the potential benefits of personalized pricing in two settings: pricing subsidies for an elective vaccine, and the effects of personalized interest rates on downstream outcomes in microcredit.
  arXiv  Detail & Related papers  (2020-12-21T01:01:56Z)
• How Value-Sensitive Design Can Empower Sustainable Consumption [2.5234156040689237]
  We demonstrate a novel personal shopping assistant implemented as a smart phone app. We use experts' knowledge and "wisdom of the crowd" for transparent product information and explainable product ratings. Real-world field experiments in two supermarkets confirm higher sustainability awareness and a bottom-up behavioral shift towards more sustainable consumption.
  arXiv  Detail & Related papers  (2020-04-20T10:11:20Z)

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.