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In news recommendation systems, eliminating redundant information is important as well as providing interesting articles for users. We propose a method that quantifies the similarity of articles based on their distributed representation, learned with the category information as weak supervision. This method is useful for evaluation under tight time constraints, since it only requires low-dimensional inner product calculation for estimating similarities. The experimental results from humandoi:10.1145/2872518.2889355 dblp:conf/www/OkuraTT16 fatcat:kjmmtldi4zg2nglpxrdeucaezi