Preserving Privacy of User Reviews in Recommender Systems

dc.contributor.advisorLarson, Martha
dc.contributor.advisorSlokom, Manel
dc.contributor.advisorHendrickx, Iris
dc.contributor.authorStax, Danny
dc.description.abstractIn this interdisciplinary thesis we combine the fields of natural language processing, recommender systems, and privacy. People tend to disclose information about themselves in user reviews, which does not benefit other users for deciding whether or not to consume an item. Such information does harm the privacy of the user and an accumulation of texts written by the user has the potential to de-anonymize a user. In this work, we have found that protecting the privacy of users by removing sensitive mentions from reviews has no substantial effect on the performance of a standard contentbased recommender model, both for the rating prediction task, and for top-N recommendation. These findings lay the foundation of using user generated texts in a responsible manner, while retaining effectively identical usability.
dc.thesis.facultyFaculteit der Sociale Wetenschappen
dc.thesis.specialisationspecialisations::Faculteit der Sociale Wetenschappen::Artificial Intelligence::Master Artificial Intelligence
dc.thesis.studyprogrammestudyprogrammes::Faculteit der Sociale Wetenschappen::Artificial Intelligence
dc.titlePreserving Privacy of User Reviews in Recommender Systems
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