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Collaborative Filtering Recommendation Algorithm based on Cluster

Volume 14, Number 5, May 2018, pp. 927-936
DOI: 10.23940/ijpe.18.05.p11.927936

Zhiyong Li

Hunan Mass Media Vocational and Technical College, Changsha, 410100, China

(Submitted on February 13, 2018; Revised on March 22, 2018; Accepted on April 25, 2018)

Abstract:

The traditional collaborative filtering recommendation method suffers from sparse datasets, cold starts, and efficiency problems. Furthermore, recommend accuracy decreases with an increase in the amount of data. Therefore, we improved the traditional collaborative filtering recommendation method by increasing the same rating between users when calculating their similarity and running it on a cluster. Because of the above actions, the collaborative filtering recommendation method obtains a better accuracy. Through experiments, we saw that the method we proposed has higher accuracy and efficiency compared to traditional collaborative filtering recommendation methods.

 

References: 12

  1.  Yaya Au, Chaomu Yuan, "Improved Collaborative Filtering Recommendation Algorithm Based on Trust Degree", The modern library and information technology, vol.10, pp.49-53, 201
  2. Haitao Chen, Shanshan Song, "Recommendation Algorithm for Collaborative Filtering Based on User", Intelligence theory and practice, vol.2, pp.100-103, 2015
  3. Shu Cheng, Lin Gui, "Subjective Rating Normalization Algorithm and Error Analysis", Journal of Higher Correspondence Education (Natural Sciences), vol.21, no.5, pp.143-147, 2011
  4. Yaoning Fang, Yunfei Guo, Lan. Hu, "An Improved Collaborative Filtering Recommendation Algorithm Based on Sigmoid Function", Computer application research, vol.5, pp. 1688-1691, 2013
  5. Wangneng Li, "Research on Improved Collaborative Filtering Recommendation Algorithm Based on Hadoop", Chongqing University, 2015
  6. Xudong Liu, Deren Chen, Huimin Wang, "An Improved Collaborative Filtering Algorithm", Journal of Wuhan University of Technology (Information Science Edition), vol.4, pp.550-553, 2010
  7. P. Melville, RJ. Mooney, R. Nagarajan, "Content-Boosted Collaborative Filtering for Improved Recommendations". In: Proc. of the 18th National Conf. on Artificial Intelligence. Menlo Park: American Association for Artificial Intelligence, pp.187−192, 2002.
  8. Hongchen Wu, Xinjun Wang, "An Improved Recommendation Algorithm Based on Collaborative Filtering and Partition Clustering", Research and development of computer science, vol. S3, pp. 205-212, 2011
  9. Junbo Wang, "Collaborative Filtering Recommendation Algorithm and Its Improvement", Chongqing University, 2010
  10. Fang Yang, Hong Pan, "An Improved Collaborative Filtering Recommendation Algorithm", Journal of Hebei University of Technology, vol.6, pp. 82-87, 2010
  11. Lanping Ye, "An Improved Collaborative Filtering Recommendation Algorithm", Anhui University, 2016
  12. Xu Zhang, "Improvement of Collaborative Filtering Recommendation Algorithm and Implementation of Distributed Computing", Shandong University, 2015

     

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