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RECO, a hybrid video recommendation system using user behaviours and features of videos

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2014
Bezen, Şükrü
If you are talking about video content on the internet, you have to think in big scales. That is why we need a system that handles the constant streaming of user behaviour on video content without delaying its recommendation system at the background. We approach to this problem by combining collaborative and content based recommendation algorithms on a framework which is completely scalable. In the thesis, we explain how we combine behaviours of users and features of videos. In what ways a change on user behaviours e ects the recommendation and how is meta data mapped to user behaviours for creating the recommendation.