Research on Commodity Recommendation Algorithm Based on RFN

  • Kai Wang
  • Bohan LiEmail author
  • Shuo Wan
  • Anman Zhang
  • Donghai Guan
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11323)


The recommendation system is one of the most widely used applications in E-commerce. By studying the user’s preferences, we can recommend underlying contents for the user from the mass merchandise information. However, most recommendation systems pay much attention on popular products, just ignore those products that are currently not popular but potential for excavation. Our recommendation system based on RFN (Reverse Furthest Neighbor) queries follows the idea of mining popular products in the niche market. We improve the traditional collaborative filtering recommendation algorithm and adopt a collaborative filtering algorithm based on expert users. The modified algorithm can recommend products with potential value based on the power law, which make the distribution of minority mined more adequately by the users. The experimental results show that the recommendation system has high recommendation quality and practical value.


Recommendation system Collaborative filtering Reverse furthest neighbor Power law 


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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Kai Wang
    • 1
  • Bohan Li
    • 1
    • 2
    • 3
    Email author
  • Shuo Wan
    • 1
  • Anman Zhang
    • 1
  • Donghai Guan
    • 1
    • 2
  1. 1.College of Computer Science and TechnologyNanjing University of Aeronautics and AstronauticsNanjingChina
  2. 2.Collaborative Innovation Center of Novel Software Technology and IndustrializationNanjingChina
  3. 3.Jiangsu Easymap Geographic Information Technology Corp., LtdYangzhouChina

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