Inproceedings,
Explanations in Open User Models for Personalized Information Exploration
Adjunct Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization, page 256–263. New York, NY, USA, Association for Computing Machinery, (Jun 28, 2024)
DOI: 10.1145/3631700.3665188
Abstract
Open user models provide affordance for a transparent user control over recommendations based on shared symbolic representation within the system. Users must build their user profile by adding these symbols and tuning their importance to get meaningful recommendations. Since the link between these symbols and the reference explanation is often unavailable, it can be difficult for users to understand them. These symbols are often referred to as concepts, tags, areas, topics, labels, features, or keyphrases. This study showcases an information exploration system that helps students identify potential faculty members to collaborate with. The system works by matching user and faculty profiles that contain keywords or phrases representing topics/areas of interest. Students must develop their understanding of research topics while building their profiles, which can become challenging as they add more keywords. To support students in controlling the recommendation, we introduce post hoc explanations with three levels of detail: no explanations, individual explanation for topics, and explanation of the relationships between topics. This study explores how explanation is associated with the user context / tasks and the exploration process. Our observation suggests that expertise in the field is linked to exploring fewer novel topics and seeking fewer explanations but engaging more with explanations of relationships. In addition, we found that the engagement with faculty information is moderately correlated with the use of more advanced explanations.
