SIG HIC - Human Computer Interaction
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Paper Type
Complete
Paper Number
1345
Description
Recommender systems tend to create filter bubbles and, as a consequence, lower diversity exposure, often with the user not being aware of it. The biased preselection of content by recommender systems has called for approaches to deal with exposure diversity, such as giving users control over their filter bubble. We analyze how to make filter bubbles understandable and controllable by using interactive word clouds, following the idea of building trust in the system. On the basis of several prototypes, we performed explorative research on how to design word clouds for the controllability of filter bubbles. Our findings can inform designers of interactive filter bubbles in personalized offers of broadcasters, publishers, and media houses.
Recommended Citation
Hirschmeier, Stefan and Ockenga, Tim Alvaro, "Making Filter Bubbles Understandable" (2022). AMCIS 2022 Proceedings. 11.
https://aisel.aisnet.org/amcis2022/sig_hci/sig_hci/11
Making Filter Bubbles Understandable
Recommender systems tend to create filter bubbles and, as a consequence, lower diversity exposure, often with the user not being aware of it. The biased preselection of content by recommender systems has called for approaches to deal with exposure diversity, such as giving users control over their filter bubble. We analyze how to make filter bubbles understandable and controllable by using interactive word clouds, following the idea of building trust in the system. On the basis of several prototypes, we performed explorative research on how to design word clouds for the controllability of filter bubbles. Our findings can inform designers of interactive filter bubbles in personalized offers of broadcasters, publishers, and media houses.
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SIG HCI