Digital Nudging with Recommender Systems: Survey and Future Directions

TL;DR

This survey explores how digital nudging can be integrated into recommender systems to influence user decisions, highlighting a large untapped potential for future research and development in this area.

Contribution

It provides a novel taxonomy of nudging mechanisms and identifies the gap in their application within recommender systems, proposing future integration strategies.

Findings

Identified 87 nudging mechanisms and categorized them into a new taxonomy.

Found that only a small fraction of nudging mechanisms have been applied to recommender systems.

Highlighted the potential for developing recommender systems that leverage digital nudging to influence user choices.

Abstract

Recommender systems are nowadays a pervasive part of our online user experience, where they either serve as information filters or provide us with suggestions for additionally relevant content. These systems thereby influence which information is easily accessible to us and thus affect our decision-making processes though the automated selection and ranking of the presented content. Automated recommendations can therefore be seen as digital nudges, because they determine different aspects of the choice architecture for users. In this work, we examine the relationship between digital nudging and recommender systems, topics that so far were mostly investigated in isolation. Through a systematic literature search, we first identified 87 nudging mechanisms, which we categorize in a novel taxonomy. A subsequent analysis then shows that only a small part of these nudging mechanisms was previously investigated in the context of recommender systems. This indicates that there is a huge potential to develop future recommender systems that leverage the power of digital nudging in order to influence the decision-making of users. In this work, we therefore outline potential ways of integrating nudging mechanisms into recommender systems.