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An Improved Parallel Collaborative Filtering Algorithm based on Hadoop

Volume 14, Number 3, March 2018, pp. 502-511
DOI: 10.23940/ijpe.18.03.p11.502511

Baojun Fu

Institute of Computer Science and Information Engineering, Harbin Normal University, Harbin, 150025, China

(Submitted on December 19, 2017; Revised on January 22, 2018; Accepted on February 17, 2018)


Abstract:

The existed parallel collaborative filtering algorithm based on co-occurrence matrix (CMCF) consumes a lot of time in the construction of co-occurrence matrixes and calculation of matrix multiplication. It also ignores the role of neighboring users, so it will influence the accuracy of recommendation. In order to solve this problem, this paper proposes the improved parallel collaborative filtering algorithm (IPCF) and its implementation on spark. The experimental results show that the improved parallel collaborative filtering algorithm in this paper has better running efficiency and higher recommendation accuracy.

 

References: 14

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  11. M. Xu, H. Shen, “Spark Parallelization Based on object Collaborative Filtering Algorithm”, Computer Engineering and Design, vol.38, no.7, pp.1817-1822,2017
  12. C. Zhang, “Research and Implementation of Hadoop Based Collaborative Filtering Algorithm”, Donghua University, 2015.
  13. T. Zhang, “An Efficient Data Clustering Method for Very Large Databases”, Proc of the 1996 ACM SIGMOD International Conference on Management of Data. New York, NY:ACM, pp.103-114,1996
  14. W. Zhao, J. Li, “Hadoop Cloud Platform Based on User Collaborative Filtering Algorithm Research”, Computer Measurement and Control, vol.23, no.6, pp.2082-2085,2015

 

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