Seeing is Believing: The Role of Scatterplots in Recommender System Trust and Decision-Making

TL;DR

This study investigates how scatterplots influence trust and decision-making in recommender systems, showing that visualizations improve accuracy perception and decision validation.

Contribution

It provides empirical evidence on the role of scatterplots in enhancing trust and decision quality in recommender systems, a previously under-explored area.

Findings

Scatterplots increase decision accuracy and trust.

Descriptive accuracy labels boost trust more than numeric labels.

Visualizations help validate user decisions.

Abstract

The accuracy of recommender systems influences their trust and decision-making when using them. Providing additional information, such as visualizations, offers context that would otherwise be lacking. However, the role of visualizations in influencing trust and decisions with recommender systems is under-explored. To bridge this gap, we conducted a two-part human-subject experiment to investigate the impact of scatterplots on recommender system decisions. Our first study focuses on high-level decisions, such as selecting which recommender system to use. The second study focuses on low-level decisions, such as agreeing or disagreeing with a specific recommendation. Our results show scatterplots accompanied by higher levels of accuracy influence decisions and that participants tended to trust the recommendations more when scatterplots were accompanied by descriptive accuracy (e.g., \textit{high}, \textit{medium}, or \textit{low}) instead of numeric accuracy (e.g., \textit{90\%}). Furthermore, we observed scatterplots often assisted participants in validating their decisions. Based on the results, we believe that scatterplots and visualizations, in general, can aid in making informed decisions, validating decisions, and building trust in recommendation systems.