Online recommender systems (RSs) are an integral element of the web used by around 4 billion people world wide. The ultimate goal of these systems is to help users find content, such as articles, videos, products, social media posts, etc. depending on the respective web service, that is in some form appealing or relevant to them.
Thesis Type |
|
Student |
Michael Kretschmer |
Status |
Finished |
Proposal on |
02/11/2021 2:00 pm |
Proposal room |
Zoom |
Presentation on |
31/05/2022 1:00 pm |
Presentation room |
Seminar room I5 6202 |
Supervisor(s) |
Ralf Klamma Stefan Decker |
Advisor(s) |
Alexander Neumann |
Contact |
neumann@dbis.rwth-aachen.de |
This is especially helpful and partially required considering the sheer amount of content on the web.
Although RSs are ubiquitous online, the attitudes expressed towards them and the knowledge of them varies greatly among their users.
While the vast majority of users seems to appreciate RSs at least to some extent, some researchers have expressed concerns that personalized recommendations narrow the exposure to diverse content which may have negative side effects.
Thus, we study diversity in recommender systems in this work by developing a system which grants users direct control over the content recommendations they receive.
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