Predicting New Adopters via Socially-Aware Neural Graph Collaborative Filtering

  • Yu-Che Tsai
  • Muzhi Guan
  • Cheng-Te Li
  • Meeyoung ChaEmail author
  • Yong Li
  • Yue Wang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11917)


We predict new adopters of specific items by proposing S-NGCF, a socially-aware neural graph collaborative filtering model. This model uses information about social influence and item adoptions; then it learns the representation of user-item relationships via a graph convolutional network. Experiments show that social influence is essential for adopter prediction. S-NGCF outperforms the prediction of new adopters compared to state-of-the-art methods by 18%.


Graph convolutional network Collaborative filtering Representation learning 



This research was partly supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT (No. NRF-2017R1E1A1A01076400), and the National Natural Science Foundation of China (Grant No. 61673237).


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Yu-Che Tsai
    • 1
    • 3
  • Muzhi Guan
    • 2
    • 3
  • Cheng-Te Li
    • 1
  • Meeyoung Cha
    • 3
    • 4
    Email author
  • Yong Li
    • 2
  • Yue Wang
    • 2
  1. 1.Department of StatisticsNational Cheng Kong UniversityTainanTaiwan
  2. 2.Department of Electronic EngineeringTsinghua UniversityBeijingChina
  3. 3.Data Science Group, IBSDaejeonSouth Korea
  4. 4.School of Computing, KAISTDaejeonSouth Korea

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