NewsViz: Depicting and controlling preference profiles using interactive treemaps in news recommender systems

Kunkel, J., Schwenger, C., & Ziegler, J. (2020). Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization, 126–135.

Thesis by Claudia Schwenger


News articles are increasingly consumed digitally and recommender systems (RS) are widely used to personalize news feeds for their users. Thereby, particular concerns about possible biases arise. When RS filter news articles opaquely, they might trap their users in filter bubbles. Additionally, user preferences change frequently in the domain of news, which is challenging for automated RS. We argue that both issues can be mitigated by depicting an interactive version of the user’s preference profile inside an overview of the entire domain of news articles. To this end, we introduce NewsViz, a RS that visualizes the domain space of online news as treemap, which can interactively be manipulated to personalize a feed of suggested news articles. In a user study (N=63), we compared NewsViz to an interface based on sliders. While both prototypes yielded high results in terms of transparency, recommendation quality and user satisfaction, NewsViz outperformed its counterpart in the perceived degree of control. Structural equation modeling allows us to further uncover hitherto underestimated influences between quality aspects of RS. For instance, we found that the degree of overview of the item domain influenced the perceived quality of recommendations.


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