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An Improved Slope One Algorithm Combining KNN Method Weighted by User Similarity

  • Songrui Tian
  • Ling OuEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9998)

Abstract

Data sparsity is a main factor affecting the prediction accuracy of collaborative filtering. Based on the simple linear regression model, Slope One algorithm aims to enhance the performance significantly by reducing the response time and maintenance, and overcoming the cold start issue. It uses rating data to do calculation without considering the similarity. In this paper, we proposed an improved algorithm by combining the dynamic k-nearest-neighborhood method and the user similarity generated by the weighted information entropy with Slope One algorithm. Especially, the similarity between users is calculated and added on the fly. Experiments on the MovieLens data set show that the proposed algorithm can achieve better recommendation quality and prediction accuracy. Besides, the stability of the algorithm is also relatively satisfying.

Keywords

Weighted information entropy K-nearest-neighborhood Slope One algorithm Personalized recommendation Data mining 

Notes

Acknowledgments

This work was supported by National Natural Science Foundations of China (No. 61170192).

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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.School of Computer and Information ScienceSouthwest UniversityChongqingChina

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