Self-supervised graph attention collaborative filtering for recommendation

HIGHLIGHTS

• who: Jiangqiang Zhu and colleagues from the School of Cyber Security and Computer, Hebei University, Baoding, China have published the research work: Self-Supervised Graph Attention Collaborative Filtering for Recommendation, in the Journal: Electronics 2023, 12, 793. of /2023/
• what: This work proposes self-supervised graph attention collaborative filtering for recommendation. The aim of contrastive learning is to increase the consistency of two jointly sampled positive sample pairs while minimizing the consistency of two dependently sampled negative sample pairs. The settings of the parameters in the model are presented, and the performance is analyzed in . . .
  

   

  

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