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Improved collaborative filtering with intensity-based contraction

  • Baojiang Cui
  • Haifeng Jin
  • Zheli Liu
  • Jiangdong Deng
Original Research

Abstract

Recommendation systems are essential tools for piquing consumers’ interests and stimulating consumption in today’s electronic commerce, and the quality of these systems depends on the employed filtering algorithms. Therefore, improving the performance of these algorithms is an important issue. In this paper, we design an intensity-based contraction (IC) algorithm that works in combination with other machine-learning algorithms in model-based collaborative filtering, which is currently the most popular filtering algorithm. The main challenges for this algorithm are sparseness of the database and lack of scalability. To demonstrate how IC is used, we implemented IC clustering as an example, which can effectively reduce the sparseness of the database and improve the efficiency. Moreover, we created a scalable IC on a MapReduce model, the scalability of which is demonstrated with actual experiments.

Keywords

Recommendation system Collaborative filtering Electronic commerce Intensity-based contraction Scalability 

Notes

Acknowledgments

This work was supported by National Natural Science Foundation of China (No.61170268).

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Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Baojiang Cui
    • 1
    • 2
  • Haifeng Jin
    • 1
    • 2
  • Zheli Liu
    • 3
    • 4
  • Jiangdong Deng
    • 1
    • 2
  1. 1.School of Computer ScienceBeijing University of Posts and TelecommunicationsBeijingChina
  2. 2.National Engineering Laboratory for Mobile Network SecurityBeijingChina
  3. 3.Department of Computer and Information Security, College of Information Technical ScienceNankai UniversityTianjinChina
  4. 4.Key Laboratory of Network Security and CryptologyFujian Normal UniversityFuzhouChina

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