Searched for: contributor:"Tintarev, Nava (mentor)"
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Kreuk, Laura (author)
Consumers share their experiences or opinion about products or brands in various channels nowadays, for example on review websites or social media. Sentiment analysis is used to predict the sentiment of text from consumers about these products or brands in order to understand the tone of customers towards these products or brands. This thesis...
master thesis 2018
document
Ghanmode, Ishan (author)
In today’s digital world, users are often confronted with an abundance of information. Whether the user is looking to compare online prices for products, searching for new movies to watch or music to listen, the available information at hand exceeds the amount of information which the user wants to consider before making a choice. For this,...
master thesis 2018
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
document
Lu, Feng (author)
Current research on personality and diversity based Recommender Systems (RecSys) are mostly separated. In most diversity-based Recommender Systems, researchers usually endeavored to achieve an optimal balance between accuracy and diversity while they commonly set a same diversity level for all users. Different diversity needs for users with...
master thesis 2018
document
Verdoorn, Joost (author)
Memes are theorized to be the building blocks of culture. Due to a lack of empirical validation, however, the theory of memes — memetics — remains in its infancy. We argue that one of the missing components for such empirical validation is a method for the large-scale identification of memes.

In this thesis, we develop a method for the...
master thesis 2018
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