|. Over the last decade, the volume of videos available on the web has increased exponentially. In order to help users cope with the ever-growing video volume, recommendation systems have emerged that can provide personalized sugges-tions to users based on their past preferences and relevant online metrics. How-ever, such approaches require user profiling, which raises privacy issues while often providing delayed suggestions as various metrics have to be firstly col-lected such as ratings and number of views. In this paper, we propose a system specifically targeting video content generated in a conference event, where a se-ries of talks and presentations are held and a separate video for each is recorded. Through audience analysis, our system is able to predict the online views of each video and thus recommend the most popular videos to users. This way, online users don’t have to search through all the videos of a conference event thus saving time while not missing the most impactful videos. The proposed system employs several complementary techniques for audience analysis based on video and audio streams. Experimental evaluation of real data demonstrates the potential of the proposed approach.|
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