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The proposed framework automatically predicts user tags for online videos from their visual features and associated textual metadata, which is semantically expanded using complementary textual resources.
Video sharing sites on the web offer a great amount of video contents, and some of them provide a facility for users to attach comments directly onto the video content. These comments let users know the content without seeing the video itself. In this paper, we propose a method for clustering the videos using the comments attached to them. We used the hybrid method of non-negative matrix factorization...
The paper proposes a method to semantically index the learning resources accessible to the learner in the social websites that he uses and bring them together in a Personal Learning Environment together with features like tag-based search and recommendations and learner profile generation. The paper describes the algorithms used, the semantic model for storing the data and the widgets that provide...
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