Research on collaborative recommendation algorithms based on parallel spectral clustering

doi:10.3969/j.issn.0253-2778.2016.01.011

Abstract

Abstract: With the increase of large-scale network data, scalability has become a key factor in the recommendation system. A new collaborative recommendation algorithm is thus based on MapReduce parallel spectral clustering was proposed. First, items are clustered using the improved parallel spectral clustering method; Then, based on the user collaborative recommendation algorithm and combined with the clustered items’ ratings, an improved calculation method for similar users is proposed to establish recommendation. The test results on the dataset show that the proposed algorithm can effectively reduce time complexity, which significantly improving its accuracy and efficiency.

Key words: recommendation system, collaborative filtering, parallel, spectral clustering

CLC Number:

TP18

ZHENG Xiumeng, CHEN Fucai, HUANG Ruiyang. Research on collaborative recommendation algorithms based on parallel spectral clustering[J]. Journal of University of Science and Technology of China, 2016, 46(1): 82-86.

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