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Network Representation Learning Based Extended Matrix Factorization for Recommendation

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Artificial Intelligence and Security (ICAIS 2020)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1254))

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Abstract

The growing heterogeneous data in the Internet effectively improves the performance of recommender systems (RS). The main problem faced by the traditional matrix factorization (MF) is how to fuse more heterogeneous information data in MF to improve the performance of RS. In view of this, this paper proposes an Extended Matrix Factorization (EMF) based on network representation learning. EMF integrates multiple types of data in Heterogeneous Information Network (HIN) to improve the accuracy of prediction rating. The nodes in HIN are first mapped to a low-dimensional representation vector by network representation learning (NRL). Then the representation vector is used as the input of the EMF, the parameters are optimized by the gradient descent, and finally the prediction model is obtained. The experiments on two real data sets show the effectiveness of the EMF. Compared with the baseline algorithms, the EMF model can obtain more accurate prediction rating.

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Authors and Affiliations

  1. State Key Laboratory of Mathematical Engineering and Advanced Computing, Zhengzhou, 450001, China

    Jinmao Xu, Daofu Gong, Fenlin Liu & Lei Tan

Authors
  1. Jinmao Xu
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  2. Daofu Gong
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  3. Fenlin Liu
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  4. Lei Tan
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Corresponding author

Correspondence to Daofu Gong .

Editor information

Editors and Affiliations

  1. Nanjing University of Information Science and Technology, Nanjing, China

    Xingming Sun

  2. Nanjing University of Information Science and Technology, Nanjing, China

    Jinwei Wang

  3. Purdue University, West Lafayette, IN, USA

    Elisa Bertino

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Xu, J., Gong, D., Liu, F., Tan, L. (2020). Network Representation Learning Based Extended Matrix Factorization for Recommendation. In: Sun, X., Wang, J., Bertino, E. (eds) Artificial Intelligence and Security. ICAIS 2020. Communications in Computer and Information Science, vol 1254. Springer, Singapore. https://doi.org/10.1007/978-981-15-8101-4_41

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  • DOI: https://doi.org/10.1007/978-981-15-8101-4_41

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-8100-7

  • Online ISBN: 978-981-15-8101-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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