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- W2359396229 abstract "To overcome the data sparsity of traditional collaborative filtering algorithm which can cause inaccuracy during finding the nearest-neighbors,the paper came up with a new method combining conditional probability with traditional collaborative algorithm whose neighbors was not always k.The algorithm' core was that the last data matrix was calculated by two-step filling.The first step,it accepted the users whose similarity and the number of both-rated items met the standard as the target user's neighbors and then calculated the value and filled the unrated-items.The second step would fill the left unrated-items relying on the data matrix filled by the first step.Experimental results show that this algorithm can find reliable neighbors,alleviate the data sparsity and achieve better prediction accuracy obviously." @default.
- W2359396229 created "2016-06-24" @default.
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- W2359396229 date "2013-01-01" @default.
- W2359396229 modified "2023-09-23" @default.
- W2359396229 title "Collaborative filtering algorithm based on two-step filling for alleviating data sparsity" @default.
- W2359396229 hasPublicationYear "2013" @default.
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