S nmf: information self-enhancement self-supervised nonnegative matrix factorization for recommendation

HIGHLIGHTS

  • who: Ronghua Zhang et al. from the Intelligent and Distributed Computing Lab, School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, China have published the research: S NMF: Information Self-Enhancement Self-Supervised Nonnegative Matrix Factorization for Recommendation, in the Journal: Wireless Communications and Mobile Computing of 30/08/2022
  • what: To address this problem the authors propose an information self-supervised NMF model for recommendation. Specifically this model is based on the matrix factorization idea and introduces a self-supervised learning mechanism based on the NMF model to enhance the . . .

     

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