Nudging Towards Sustainable Choices via Recommender Systems

Nudging Towards Sustainable Choices via Recommender Systems

Kalisvaart, Raoul (TU Delft Electrical Engineering, Mathematics and Computer Science)

Isufi, E. (mentor)
Scharenborg, O.E. (graduation committee)
Cruz, Luis (graduation committee)

Delft University of Technology

Delft University of Technology

Computer Science | Multimedia Computing

2022-08-24

We all know the possible consequences of global warming, rising temperatures, flooded cities and destroyed ecosystems. One of the causes is the emission of gases, predominantly CO2, which is increased by the growing E-commerce market. E-commerce companies rely on recommender systems to stimulate users to purchase products. We are convinced that we can use the core strength of recommender systems, influencing decision making, to steer users towards eco-friendly choices. Therefore, in this thesis, we research how greenness can be integrated into recommender systems. We present the first recommender system dataset that includes greenness, we benchmark several recommendation algorithms and we propose a strategy to increase recommendation greennness. To create the dataset, we annotate an existing recipe recommendation dataset with recipe greenness. For our benchmarking experiment, we propose metrics to measure recommendation greenness, which we use to show that no recommendation algorithm is fundamentally greener than others. Lastly, we propose a re-ranking method for improving the greenness of recommendation rankings. We use the method to explore the trade-off between accuracy and greenness and we show that it is possible improve the greenness of recommender systems significantly with little loss of accuracy.

Recommender Systems
Nudging
Machine Learning for Climate Change
Sustainability

http://resolver.tudelft.nl/uuid:dede59cc-5cd8-4420-b6ba-f6002f965b7c

2023-08-24

Student theses

master thesis

© 2022 Raoul Kalisvaart