A Fast Interactive Item-Based Collaborative Filtering Algorithm

  • Zhenyan Ji
  • Zhi Zhang
  • Canzhen Zhou
  • Haishuai Wang
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 768)


A recommender system becomes more and more popular in e-commerce. Usually prediction results cannot satisfy users’ requirements fully, and sometimes it even contains totally irrelevant items. To reflect users’ newest preference and increase the quality of recommendation, a fast interactive item-based collaborative filtering algorithm is proposed. Firstly, we propose an item-based collaborative filtering algorithm with less time and space complexity. Then we introduce interactive iterations to reflect users’ up-to-date preference and increase users’ satisfaction. The experiments show that our fast interactive item-based CF algorithm has better recall and precision than traditional item-based CF algorithm.


Recommender system Item-based collaborative filtering Interactive recommendation Iteration 


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Copyright information

© Springer Nature Singapore Pte Ltd. 2017

Authors and Affiliations

  • Zhenyan Ji
    • 1
  • Zhi Zhang
    • 1
  • Canzhen Zhou
    • 1
  • Haishuai Wang
    • 2
  1. 1.Beijing Jiaotong UniversityHaidianChina
  2. 2.Washington University in St. LouisSt. LouisUSA

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