Journal of East China Normal University(Natural Sc ›› 2019, Vol. 2019 ›› Issue (3): 63-77.doi: 10.3969/j.issn.1000-5641.2019.03.008

• Computer Science • Previous Articles     Next Articles

Research on a commodity recommendation algorithm based on reverse furthest neighbor

WANG Kai1, LI Bo-han1,2,3, WAN Shuo1, ZHANG An-man1, GUAN Dong-hai1   

  1. 1. College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China;
    2. Collaborative Innovation Center of Novel Software Technology and Industrialization, Nanjing 211106, China;
    3. Jiangsu Easymap Geographic Information Technology Corp. Ltd, Yangzhou Jiangsu 225000, China
  • Received:2018-08-03 Online:2019-05-25 Published:2019-05-30

Abstract: Effective recommendation algorithms can help maximize the value of a product. By studying the user's preferences, we can recommend underlying contents for the user from mass merchandise information. However, most recommendation systems focus on popular products, ignoring those products that are currently not popular but have huge potential. Our recommendation system, based on reverse furthest neighbor (RFN) queries, uses the idea of mining popular products in niche markets. 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 makes the distribution of minority mined products more visible to users. Experimental results show that the quality of the proposed algorithm is high and is suitable for partially addressing the long tail problem.

Key words: recommendation system, collaborative filtering, reverse furthest neighbor (RFN), power law

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