document: Video suggestion and discovery for youtube : taking random walks through the view graph The rapid growth of the number of videos in YouTube provides enormous potential for users to find content of interest to them . Unfortunately , given the difficulty of searching videos , the size of the video repository also makes the discovery of new content a daunting task . In this paper , we present a novel method based upon the analysis of the entire user-video graph to provide personalized video suggestions for users . The resulting algorithm , termed Adsorption , provides a simple method to efficiently propagate preference information through a variety of graphs . We extensively test the results of the recommendations on a three month snapshot of live data from YouTube .
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