Visualization for Recommendation Explainability: A Survey and New Perspectives

Providing system-generated explanations for recommendations represents an important step towards transparent and trustworthy recommender systems. Explainable recommender systems provide a human-understandable rationale for their outputs. Over the last two decades, explainable recommendation has attracted much attention in the recommender systems research community. This paper aims to provide a comprehensive review of research efforts on visual explanation in recommender systems. More concretely, we systematically review the literature on explanations in recommender systems based on four dimensions, namely explanation goal, explanation scope, explanation style, and explanation format. Recognizing the importance of visualization, we approach the recommender system literature from the angle of explanatory visualizations, that is using visualizations as a display style of explanation. As a result, we derive a set of guidelines that might be constructive for designing explanatory visualizations in recommender systems and identify perspectives for future work in this field. The aim of this review is to help recommendation researchers and practitioners better understand the potential of visually explainable recommendation research and to support them in the systematic design of visual explanations in current and future recommender systems.Comment: Updated version Nov. 2023, 36 page

Effects of Individual Traits on Diversity-aware Music Recommender User Interfaces

When recommendations become increasingly personalized, users are often presented with a narrower range of content. To mitigate this issue, diversity-enhanced user interfaces for recommender systems have in the past found to be effective in increasing overall user satisfaction with recommendations. However, users may have different requirements for diversity, and consequently different visualization requirements. In this paper, we evaluate two visual user interfaces, SimBub and ComBub, to present the diversity of a music recommender system from different perspectives. SimBub is a baseline bubble chart that shows music genres and popularity by color and size, respectively. In addition, ComBub visualizes selected audio features along the X and Y axis in a more advanced and complex visualization. Our goal is to investigate how individual traits such as musical sophistication (MS) and visual memory (VM) influence the satisfaction of the visualization for perceived music diversity, overall usability, and support to identify blind-spots. We hypothesize that music experts, or people with better visual memory, will perceive higher diversity in ComBub than SimBub. A within-subjects user study (N=83) is conducted to compare these two visualizations. Results of our study show that participants with high MS and VM tend to perceive significantly higher diversity from ComBub compared to SimBub. In contrast, participants with low MS perceived significantly higher diversity from SimBub than ComBub; however, no significant result is found for the participants with low VM. Our research findings show the necessity of considering individual traits while designing diversity-aware interfaces.Accepted author manuscriptWeb Information System

Effects of Individual Traits on Diversity-Aware Music Recommender User Interfaces

© 2018 Association for Computing Machinery. When recommendations become increasingly personalized, users are often presented with a narrower range of content. To mitigate this issue, diversity-enhanced user interfaces for recommender systems have in the past found to be effective in increasing overall user satisfaction with recommendations. However, users may have different requirements for diversity, and consequently different visualization requirements. In this paper, we evaluate two visual user interfaces, SimBub and ComBub, to present the diversity of a music recommender system from different perspectives. SimBub is a baseline bubble chart that shows music genres and popularity by color and size, respectively. In addition, ComBub visualizes selected audio features along the X and Y axis in a more advanced and complex visualization. Our goal is to investigate how individual traits such as musical sophistication (MS) and visual memory (VM) influence the satisfaction of the visualization for perceived music diversity, overall usability, and support to identify blind-spots. We hypothesize that music experts, or people with better visual memory, will perceive higher diversity in ComBub than SimBub. A within-subjects user study (N=83) is conducted to compare these two visualizations. Results of our study show that participants with high MS andVMtend to perceive significantly higher diversity from ComBub compared to SimBub. In contrast, participants with low MS perceived significantly higher diversity from SimBub than ComBub; however, no significant result is found for the participants with low VM. Our research findings show the necessity of considering individual traits while designing diversity-aware interfaces.status: publishe