Advertisement

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)

Abstract

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%.

Keywords

Graph convolutional network Collaborative filtering Representation learning 

Notes

Acknowledgement

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).

References

  1. 1.
    Cha, M., Haddadi, H., Benevenuto, F., Gummadi, K.P.: Measuring user influence in Twitter: the million follower fallacy. In: Proceedings of the ICWSM (2010)Google Scholar
  2. 2.
    De Meo, P., Ferrara, E., Fiumara, G., Provetti, A.: Generalized Louvain method for community detection in large networks. In: Proceedings of the ISDA (2011)Google Scholar
  3. 3.
    He, X., Liao, L., Zhang, H., Nie, L., Hu, X., Chua, T.S.: Neural collaborative filtering. In: Proceedings of the WWW (2017)Google Scholar
  4. 4.
    Kang, Z., Peng, C., Cheng, Q.: Top-N recommender system via matrix completion. In: Proceedings of the AAAI (2016)Google Scholar
  5. 5.
    Kelman, H.C.: Compliance, identification, and internalization three processes of attitude change. J. Confl. Resolut. 2(1), 51–60 (1958)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Kempe, D., Kleinberg, J., Tardos, E.: Maximizing the spread of influence through a social network. In: Proceedings of the KDD (2003)Google Scholar
  7. 7.
    Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)
  8. 8.
    Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. In: Proceedings of the ICLR (2017)Google Scholar
  9. 9.
    Leskovec, J., Adamic, L.A., Huberman, B.A.: The dynamics of viral marketing. ACM Trans. Web 1(1), 5 (2007)CrossRefGoogle Scholar
  10. 10.
    Liu, Y., Wei, W., Sun, A., Miao, C.: Exploiting geographical neighborhood characteristics for location recommendation. In: Proceedings of the CIKM (2014)Google Scholar
  11. 11.
    Rendle, S., Freudenthaler, C., Gantner, Z., Schmidt-Thieme, L.: BPR: Bayesian personalized ranking from implicit feedback. In: Proceedings of the UAI (2009)Google Scholar
  12. 12.
    Wang, X., He, X., Wang, M., Feng, F., Chua, T.S.: Neural graph collaborative filtering. In: Proceedings of the SIGIR (2019)Google Scholar

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

Personalised recommendations