An Improved Parallel Collaborative Filtering Algorithm based on Hadoop

doi:10.23940/ijpe.18.03.p11.502511

Abstract

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.

Submitted on December 19, 2017; Revised on January 22, 2018; Accepted on February 17, 2018
References: 14

Baojun Fu. An Improved Parallel Collaborative Filtering Algorithm based on Hadoop [J]. Int J Performability Eng, 2018, 14(3): 502-511.

Add to citation manager EndNote|Reference Manager|ProCite|BibTeX|RefWorks

References 0

	G. Bart. “Memory Issues in Frequent Itemset Mining”. Proc of ACM Symposium on Applied Computing, New York,NY:ACM, pp.530-534,2004
	C. Cheng, “Research on Cloud Platform Recommendation Algorithm”, Chongqing University of Technology, 2014.
	M. Ester, Hans-peter. Krieger, “A Density-based Algorithm for Discovering Clusters in Large Spatial Databases with Noise”, Proc of the Second International Conference on Knowledge Discovery and Data Mining. Menlo Park, California: AAAI Press. pp. 226-231, 1996
	S. Gill. “Introduction to Modern Information Retrieval”, Mc Graw-Hill, New York,NY,USA,1983.
	L. Herlocker, “A Collaborative Filtering Algorithm and Evaluation Metric That Accurately Model the User Experience”, in Proceedings of 27th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Sheffield, UK, pp.329-336, 2004.
	C. Li, “Recommendation Algorithm and Application of MapReduce Based on Hybrid”, Computer Technology and Development,vol.26, no.4, pp. 74-77,2016
	“Movie Lens: Film Recommendations”, Http://movielens.umn.edu.
	L. Qi, “Research on Collaborative Filtering Algorithm Based on MapReduce”, Taiyuan University of Technology, 2014.
	B. Tian, P. Hu. “Research on Collaborative Filtering Recommendation Algorithm Based on clustering,” Computer Engineering and Science, vol.38, no. 8, pp. 1615-1624,2016
	Y. Wen, D. Wu, “Personalized Education Resources in The Spark Platform”, The Intelligent Computer and Application, vol.7, no. 2, pp.25-30,2017
	M. Xu, H. Shen, “Spark Parallelization Based on object Collaborative Filtering Algorithm”, Computer Engineering and Design, vol.38, no.7, pp.1817-1822,2017
	C. Zhang, “Research and Implementation of Hadoop Based Collaborative Filtering Algorithm”, Donghua University, 2015.
	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
	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