Optimization of Slope One Algorithm Based on Clustering and Project Similarity

doi:10.3969/j.issn.1006-2475.2016.08.005

Abstract

Abstract: With the growth of number of users, the user item matrix becomes more and more sparse. K-means algorithm based on minimum spanning tree of the project is used to cluster the items. The clustered results for the user rating matrix are used to predict filling. Taking into account the user interest change in Slope One algorithm, time weight added to the Slope One algorithm is used to predict rating. Experiments on the Movie Lens dataset show that the improved algorithm effectively solves the sparse and user interest change problem and the MAE value is reduced to less than 0.015.

Key words: Slope One, k-means, sparsity, interest change

CLC Number:

TP391

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JIANG Zong-li, DU Qian. Optimization of Slope One Algorithm Based on Clustering and Project Similarity[J]. Computer and Modernization, doi: 10.3969/j.issn.1006-2475.2016.08.005.

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