Searched for: subject:"Filter%5C+bubble"
(1 - 4 of 4)
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
document
Rothweiler, Joost (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
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Simes, A. (author)
When talking about personalization online, Google CEO Eric Schmidt recently said "it will be very hard for people to watch or consume something that has not in some sense been tailored for them." This level of personalized filtering of content has worried academics and activists. Many argue that users will be trapped in a so-called "Filter...
master thesis 2016
Searched for: subject:"Filter%5C+bubble"
(1 - 4 of 4)