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Jaccard Coefficient-Based Bi-clustering and Fusion Recommender System for Solving Data Sparsity

  • Jiangfei Cheng
  • Li ZhangEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11440)

Abstract

Recommender systems have been very common and useful nowadays, which recommend suitable items to users by predicting ratings for items. The most used collaborative filtering recommender system suffers from the sparsity issue due to insufficient data. To cope with this issue, we propose a Jaccard Coefficient-based Bi-clustering and Fusion (JC-BiFu) method for Recommender system. JC-BiFu uses density peak clustering for both users and items, and then makes estimations for missing values in the user-item rating matrix when finding the similar users. Finally, we utilize both users and items to generate the final predictions. Experimental analysis shows that our approach can improve the performance of user recommendations at the extreme levels of sparsity in user-item rating matrix.

Keywords

Collaborative filtering Recommender system Jaccard coefficient Cluster Data sparsity 

Notes

Acknowledgement

This work was supported in part by the National Natural Science Foundation of China under Grant No. 61373093, by the Soochow Scholar Project, by the Six Talent Peak Project of Jiangsu Province of China, and by the Collaborative Innovation Center of Novel Software Technology and Industrialization.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Computer Science and TechnologySoochow UniversitySuzhouChina
  2. 2.Provincial Key Laboratory for Computer Information Processing TechnologySoochow UniversitySuzhouChina

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