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Research on Cold-Start Problem in User Based Collaborative Filtering Algorithm

  • Lu LiuEmail author
  • Zhiqian Wang
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 905)

Abstract

In order to solve the cold-start problem existing in traditional user based collaborative filtering algorithm, we propose a novel user clustering based algorithm, which firstly prefills user-item rating matrix, and then considers user characteristics as well as ratings when computing user similarities, and applies optimized k-means algorithm to cluster users. MovieLens is used as the test dataset. It is proved that the algorithm proposed in this paper can solve the cold-start problem and improve the accuracy of recommendation to some extent.

Keywords

Cold-start Collaborative filtering K-means user clustering Similarity calculation optimization 

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Beijing University of Posts and TelecommunicationsBeijingChina

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