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

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Advances in Knowledge Discovery and Data Mining (PAKDD 2019)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11440))

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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.

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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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Authors and Affiliations

  1. Department of Computer Science and Technology, Soochow University, Suzhou, China

    Jiangfei Cheng & Li Zhang

  2. Provincial Key Laboratory for Computer Information Processing Technology, Soochow University, Suzhou, China

    Li Zhang

Authors
  1. Jiangfei Cheng
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  2. Li Zhang
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Corresponding author

Correspondence to Li Zhang .

Editor information

Editors and Affiliations

  1. Hong Kong University of Science and Technology, Hong Kong, China

    Qiang Yang

  2. Nanjing University, Nanjing, China

    Zhi-Hua Zhou

  3. University of Macau, Taipa, Macau, China

    Zhiguo Gong

  4. Southeast University, Nanjing, China

    Min-Ling Zhang

  5. Nanjing University of Aeronautics and Astronautics, Nanjing, China

    Sheng-Jun Huang

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Cheng, J., Zhang, L. (2019). Jaccard Coefficient-Based Bi-clustering and Fusion Recommender System for Solving Data Sparsity. In: Yang, Q., Zhou, ZH., Gong, Z., Zhang, ML., Huang, SJ. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2019. Lecture Notes in Computer Science(), vol 11440. Springer, Cham. https://doi.org/10.1007/978-3-030-16145-3_29

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  • DOI: https://doi.org/10.1007/978-3-030-16145-3_29

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-16144-6

  • Online ISBN: 978-3-030-16145-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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