Abstract:Classification of label-specific users’ diversified interests is the most formidable task in personalized news recommendation systems (PNRS). To bring personalization to PNRS, many remarkable features have to be considered from their user profile to classify their interest. In this paper, 13, 346 features are considered per user to classify their interest for 15 labels using Multi-label Convolution Neural Network (MLCNN). The efficiency of MLCNN highly depends on its architecture through the tuning of its hype… Show more
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