  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
personal and societal 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.
