Contextual Personalized Re-Ranking of Music Recommendations through Audio Features Based User Preference Models

With advancements in Internet and technology, it has become increasingly easy for people to enjoy music. Users are able to access millions of songs through music streaming services like Spotify, Pandora, and Deezer. Access to such large catalogs created a need for relevant song recommendations. Music recommender systems assist users in finding...

Exploring Exploration in Recommender Systems: Where? How Much? For Whom?

Recommender systems focus on automatically surfacing suitable items for users from digital collections that are too large for the user to oversee themselves. A considerable body of work exists on surfacing items that match what a user liked in the past; this way, the recommender system will exploit its knowledge of a user's comfort zone. However...

Politags: Breaking the filter bubble by using human computation techniques to enable diverse personalization

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...

Personalization on a peer-to-peer television system

We introduce personalization on Tribler, a peer-to-peer (P2P) television system. Personalization allows users to browse programs much more efficiently according to their taste. It also enables to build social networks that can improve the performance of current P2P systems considerably, by increasing content availability, trust and the...