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Matrix Factorization Approach Based on Temporal Hierarchical Dirichlet Process

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Book cover Intelligence Science and Big Data Engineering. Big Data and Machine Learning Techniques (IScIDE 2015)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 9243))

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Abstract

User-based collaborative filtering algorithms generally rely on the users’ stationary preferences, yet user preferences in real world are seldom stationary. User preference patterns may have the time-evolving statistical properties in many social contexts. Motivated by this phenomenon, we propose a temporal collaborative filtering approach based on temporal Hierarchical Dirichlet Process (tHDP). This approach can capture the density changes on the time-evolving datasets. Experiments on large real world datasets demonstrate the superiority of our proposed approach.

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Author information

Authors and Affiliations

  1. College of Computer, National University of Defense Technology, Changsha, People’s Republic of China

    Liang Chen & Peidong Zhu

Authors
  1. Liang Chen
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  2. Peidong Zhu
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Corresponding author

Correspondence to Liang Chen .

Editor information

Editors and Affiliations

  1. Zhejiang University, Hangzhou, China

    Xiaofei He

  2. Xidian University, Xi'an, China

    Xinbo Gao

  3. Northwestern Polytechnical University, Xi'an, China

    Yanning Zhang

  4. Nanjing University, Nanjing, China

    Zhi-Hua Zhou

  5. Chinese Academy of Sciences, Beijing, China

    Zhi-Yong Liu

  6. Suzhou University of Science and Technology, Suzhou, China

    Baochuan Fu

  7. Suzhou University of Science and Technology, Jiangsu, China

    Fuyuan Hu

  8. Suzhou University of Science and Technology, Jiangsu, China

    Zhancheng Zhang

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© 2015 Springer International Publishing Switzerland

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Chen, L., Zhu, P. (2015). Matrix Factorization Approach Based on Temporal Hierarchical Dirichlet Process. In: He, X., et al. Intelligence Science and Big Data Engineering. Big Data and Machine Learning Techniques. IScIDE 2015. Lecture Notes in Computer Science(), vol 9243. Springer, Cham. https://doi.org/10.1007/978-3-319-23862-3_20

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  • DOI: https://doi.org/10.1007/978-3-319-23862-3_20

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-23861-6

  • Online ISBN: 978-3-319-23862-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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