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research-article

In-processing and post-processing strategies for balancing accuracy and sustainability in product recommendations

Authors:

François Fouss,

Pierre LeleuxAuthors Info & Claims

Volume 67, Issue C

https://doi.org/10.1016/j.elerap.2024.101433

Published: 18 November 2024 Publication History

Highlights

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This paper aims to promote sustainable products in recommendations without accuracy loss.

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We propose three in-processing (IP) and four post-processing (PP) strategies.

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Some PP strategies manage to offer interesting accuracy-sustainability trade-offs.

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The calibration PP strategy can give a 20% sustainability gain without accuracy loss.

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Higher sustainability improvements can be achieved if a loss of accuracy is tolerated.

Abstract

Many e-commerce websites use product recommendation systems. With the growing awareness of the environmental impact of our consumption, these recommender systems, well-known for encouraging purchases and consumption, are being challenged. In recent literature, it has been suggested that recommender systems should balance the exploitation of existing preferences with the exploration of sustainable items, i.e., to make sustainable alternatives more accessible to consumers and promote sustainable consumption habits. In this paper, we therefore analyse how to increase the presence of sustainable products in recommendation lists, without overly decreasing their accuracy. More precisely, we test three in-processing and four post-processing strategies using an offline experimental design. The post-processing strategies 1 (relevance scores’ adjustment) and 3.1. (incremental list formation with calibration) manage to offer interesting accuracy-sustainability trade-offs on our datasets. For instance, by applying post-processing strategy 3.1 to a content-based recommendation algorithm, a gain of up to 20% can be achieved for the sustainability metric without any loss of accuracy. Greater sustainability improvements can be achieved if a loss of accuracy is tolerated. For practitioners, i.e., e-commerce platforms, this means that they could continue to offer relevant recommendations while promoting a more sustainable consumption.

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Index Terms

In-processing and post-processing strategies for balancing accuracy and sustainability in product recommendations
1. Applied computing
  1. Operations research
    1. Decision analysis
2. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
  2. Information systems applications
    1. Decision support systems

Index terms have been assigned to the content through auto-classification.

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cover image Electronic Commerce Research and Applications

Electronic Commerce Research and Applications Volume 67, Issue C

Sep 2024

341 pages

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Elsevier B.V.

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Elsevier Science Publishers B. V.

Netherlands

Publication History

Published: 18 November 2024

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