Context aware recommendations for user-generated content on a social network site

The enormous offer of video content on the internet and its continuous growth make the selection process increasingly difficult for end-users. This overabundance of audio-visual material can be handled by a recommendation system that observes user preferences and assists people with finding interesting content. However, present-day recommendation systems focus on the metadata or the previous consumption behaviour to select the content but do not consider contextual information or social network relations. Therefore, we developed a tag cloud based recommendation system for user-generated content which exploits these social network relations. Recommendations based on the user's profile are supplemented with social recommendations: content suggestions from people on the user's contact list. Moreover, since we believe that the consumption context (location, time, etc.) has a significant influence on the content selection process, the system records all the available context information. Our next task is to analyze the obtained dataset and to determine the influence of the individual context features on the consumption behaviour. The system recommendations and social recommendations will be compared on the basis of effectiveness, novelty and user appreciation. Finally, we intend to incorporate the results of this analysis in our personalization algorithm in order to improve the recommendation results.