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Knowledge discovery over community-sharing media: from signal to intelligence
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Generalized Expectation Criteria for Semi-Supervised Learning with Weakly Labeled Data
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A link prediction approach to recommendations in large-scale user-generated content systems
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Scene extraction system for video clips using attached comment interval and pointing region
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Automatic selection of radiological protocols using machine learning
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A hybrid video recommendation system using a graph-based algorithm
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Learning recommendations in social media systems by weighting multiple relations
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PrIter: a distributed framework for prioritized iterative computations
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Opinion target extraction using partially-supervised word alignment model
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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.