Username   Password       Forgot your password?  Forgot your username? 

ISSUES BY YEAR

Volume 14 - 2018

No.1 January 2018
No.1 January 2018
No.3 March 2018
No.3 March 2018
No.4 April 2018
No.4 April 2018
No.5 May 2018
No.5 May 2018
No.6 June 2018
No.6 June 2018
No.7 July 2018
No.7 July 2018

Volume 13 - 2017

No.4 July 2017
No.4 July 2017
No.5 September 2017
No.5 September 2017
No.7 November 2017
No.7 November 2017
No.8 December 2017
No.8 December 2017

Volume 12 - 2016

Volume 11 - 2015

Volume 10 - 2014

Volume 9 - 2013

Volume 8 - 2012

Volume 7 - 2011

Volume 6 - 2010

Volume 5 - 2009

Volume 4 - 2008

Volume 3 - 2007

Volume 2 - 2006

 

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

  1. G. Bart. “Memory Issues in Frequent Itemset Mining”. Proc of ACM Symposium on Applied Computing, New York,NY:ACM, pp.530-534,2004
  2. C. Cheng, “Research on Cloud Platform Recommendation Algorithm”, Chongqing University of Technology, 2014.
  3. 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
  4. S. Gill. “Introduction to Modern Information Retrieval”, Mc Graw-Hill, New York,NY,USA,1983.
  5. 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.
  6. C. Li, “Recommendation Algorithm and Application of MapReduce Based on Hybrid”, Computer Technology and Development,vol.26, no.4, pp. 74-77,2016
  7. “Movie Lens: Film Recommendations”, Http://movielens.umn.edu.
  8. L. Qi, “Research on Collaborative Filtering Algorithm Based on MapReduce”, Taiyuan University of Technology, 2014.
  9. 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
  10. Y. Wen, D. Wu, “Personalized Education Resources in The Spark Platform”, The Intelligent Computer and Application, vol.7, no. 2, pp.25-30,2017
  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

 

Please note : You will need Adobe Acrobat viewer to view the full articles.Get Free Adobe Reader

Attachments:
Download this file (IJPE-2018-03-11.pdf)IJPE-2018-03-11.pdf[An Improved Parallel Collaborative Filtering Algorithm based on Hadoop]580 Kb
 

CURRENT ISSUE

Prev Next

Data Packet Processing Model based on Multi-Core Architecture

Xian Zhang, Dong Yin, Taiguo Qu, Jia Liu, and Yiwen Liu

Read more

Mixed Weighted KNN for Imbalanced Datasets

Qimin Cao, Lei La, Hongxia Liu, and Si Han

Read more

Query Expansion based on Naive Bayes and Semantic Similarity

Zhiyun Zheng, Mengyao Yu, Ning Wang, Xingjin Zhang, Chunyang Ruan, and Dun Li

Read more

Automated Collaborative Analysis System of Rockburst Mechanism based on Big Data

Yu Zhang, Hongwei Ding, Yange Wang, Fuqiang Ren, Yongzhen Li, and Zhaoyong Lv

Read more

EOR of Spontaneous Imbibition by Surfactant Solution for Tight Oil Reservoirs

Anqi Shen, Yikun Liu, Shuang Liang, Fengjiao Wang, Bo Cai, and Yuebin Gao

Read more

Dynamic Community Mining based on Behavior Prediction

Xiao Chen, Xinzhuan Hu, Xiao Pan, and Jingfeng Guo

Read more

Lithium-ion Power Batteries SOC Estimation based on PCA

Haiying Wang, Yuran Wang, Zhilin Yao, and Zhilong Yu

Read more
This site uses encryption for transmitting your passwords. ratmilwebsolutions.com