Searched for: subject:"filters"
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document
Kumar, Jaya (author)
In recent years, personalized recommender systems have been facing criticism in research due to their ability to trap users in their circle of choices, called "filter-bubble", thereby limiting their exposure to novel content. In solving the issue of filter-bubble, past research has focused on providing explanations to users about how a...
master thesis 2018
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van Zoest, Max (author)
This research is aimed at breaking online political filter bubbles. We present a system that uses artificial intelligence and human computation to automatically collect article metadata on political content and thereby enables diverse personalization on content-serving platforms. This way, exploring alternative viewpoints could become as simple...
master thesis 2018