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Because users hardly have patience of affording enough labeled data, personalized filter is expected to converge much faster. Topic model based dimension reduction can minimize the structural risk with limited training data. In this paper, we propose a novel supervised dual-PLSA which estimate topics with many kinds of observable data, i.e. labeled and unlabeled documents, supervised information about...
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