Concentrating on the Impact: Consequence-based Explanations in Recommender Systems

TL;DR

This paper introduces consequence-based explanations in recommender systems, emphasizing individual impacts of recommendations to improve user understanding and satisfaction, supported by an online user study.

Contribution

It proposes a novel consequence-based explanation method and empirically evaluates its effectiveness in enhancing user experience.

Findings

Consequence-based explanations are well-received by users.

They significantly improve user satisfaction.

The approach clarifies the impact of recommendations.

Abstract

Recommender systems assist users in decision-making, where the presentation of recommended items and their explanations are critical factors for enhancing the overall user experience. Although various methods for generating explanations have been proposed, there is still room for improvement, particularly for users who lack expertise in a specific item domain. In this study, we introduce the novel concept of \textit{consequence-based explanations}, a type of explanation that emphasizes the individual impact of consuming a recommended item on the user, which makes the effect of following recommendations clearer. We conducted an online user study to examine our assumption about the appreciation of consequence-based explanations and their impacts on different explanation aims in recommender systems. Our findings highlight the importance of consequence-based explanations, which were well-received by users and effectively improved user satisfaction in recommender systems. These results provide valuable insights for designing engaging explanations that can enhance the overall user experience in decision-making.