Understanding the relationship between user emotion and latent musical features

With the advent of Internet and resulting data boom, Recommender Systems have come to rescue by filtering the information available on the internet by providing us with relevant information. These systems come handy when one wants to listen to songs, watch movies or even buy products on the Internet. Primarily, these recommender systems used...

From log files to train traffic reports : Using Natural Language Generation to explain anomalies from Train Control System log files

The Natural Language Generation field has advanced in generating human readable reports for domain experts in various fields. Nevertheless, Natural Language Generation and anomaly detection techniques have not been used in the rail domain yet. Currently, data analysis and incident reporting for log files from the train control system are...

Sentiment Analysis: a comparison of feature sets for social data and reviews

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...

Leveraging Social Information To Improve Recommendation Novelty

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,...

Enhancing the Diversity Adjusting Strategy with Personality Information in Music Recommender Systems

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...

Breaking the filter-bubble: Using visualizations to encourage blind-spots exploration

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...

Identifying memes and their interactions in online communities

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.<br/><br/>In this thesis, we develop a method for the...