Mobile APP User Attribute Prediction by Heterogeneous Information Network Modeling

  • Hekai Zhang
  • Jibing GongEmail author
  • Zhiyong Teng
  • Dan Wang
  • Hongfei Wang
  • Linfeng Du
  • Zakirul Alam Bhuiyan
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1123)


User-based attribute information, such as age and gender, is usually considered as user privacy information. It is difficult for enterprises to obtain user-based privacy attribute information. However, user-based privacy attribute information has a wide range of applications in personalized services, user behavior analysis and other aspects. Although many scholars have made achievements in user attribute prediction and other related fields, there are still two main problems that impede further improvement on the accuracy of classification: (1) Traditional machine learning classification merely takes each object as a single individual, ignoring the relationship between them; (2) At present, the popular Heterogeneous Path-Mine Information Network only considers whether the user has a relationship with the attributes of other nodes, rather than the degree of correlation of the attributes. It employs a linear regression model to fit the weight of meta-path, which is highly sensitive to outliers. To solve the above two problems, this paper advances the HetPathMine model and puts forward TPathMine model. With applying the number of clicks of attributes under each node to express the user’s emotional preference information, optimizations of the solution of meta-path weight are also presented. Based on meta-path in heterogeneous information networks, the new model integrates all relationships among objects into isomorphic relationships of classified objects. Matrix is used to realize the knowledge dissemination of category knowledge among isomorphic objects. The experimental results show that: (1) the prediction of user attributes based on heterogeneous information networks can achieve higher accuracy than traditional machine learning classification methods; (2) TPathMine model based on the number of clicks is more accurate in classifying users of different age groups, and the weight of each meta-path is consistent with human intuition or the real world situation.


Classification algorithm Heterogeneous information network Meta-path User attribute prediction Attention mechanism 


  1. 1.
    Chen, J., Li, S.S., Wang, J., et al.: User age recognition based on hybrid classification/regression model. Chin. Sci. Inf. Sci. 08, 147–160 (2017)Google Scholar
  2. 2.
    Wang, Y., Xia, Y., et al.: Prediction of demographic information of mobile users. J. Univ. Electron. Sci. Technol. 44(6), 917–920 (2015)Google Scholar
  3. 3.
     Peng, H., et al.: Finegrained event categorization with heterogeneous graph convolutional networks. In: Proceedings of the 28th International Joint Conference on Artificial Intelligence, pp. 3238–3245. AAAI Press (2019)Google Scholar
  4. 4.
    Liu, Y., et al.: Event detection and evolution based on knowledge base. In: 2018 KBCOM (2018)Google Scholar
  5. 5.
    Weber, I.,Castillo, C.: The demographics of web search. In: SIGIR’10: Proceedings of the 33rd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 523–530. ACM, New York (2010)Google Scholar
  6. 6.
    Xu, X., Wang, J., Peng, H., Wu, R.: Prediction of academic performance associated with internet usage behaviors using machine learning algorithms. Comput. Hum. Behav. 98, 166–173 (2019)CrossRefGoogle Scholar
  7. 7.
    Bi, B., Kosinski, M., Shokouhi, M., et al.: Inferring the demographics of search users social data meets search queries. ACM (2013)Google Scholar
  8. 8.
    He, Y.,  Song, Y., Li, J.,  Ji, C., Peng,  J., Peng, H.: Hetespaceywalk: a heterogeneous spacey random walk for heterogeneous information network embedding. In: Proceedings of the 28th ACM International Conference on Information and Knowledge Management. ACM (2019)Google Scholar
  9. 9.
    Liu, Y., Peng, H., Li, J., Song, Y., Li, X.: Event detection and evolution in multi-lingual social streams. Frontiers of Computer Science (2019)Google Scholar
  10. 10.
    Statnikov, A., Aliferis, C.F., Hardin, D.P., et al.: Support Vector Regression (SVR). A Gentle Introduction to Support Vector Machines in Biomedicine: Volume 1: Theory and MethodsGoogle Scholar
  11. 11.
    Yang, Y., Tang, J., Li, J., et al.: Learning to infer competitive relationships in heterogeneous networks. ACM Trans. Knowl. Discov. Data 12(1), 1–23 (2018)CrossRefGoogle Scholar
  12. 12.
    Du, B., et al.: Deep irregular convolutional residual LSTM for urban traffic passenger flows prediction. IEEE Trans. Intell. Transp. Syst. (2019)Google Scholar
  13. 13.
    Zhou, C.: Classification of heterogeneous information networks. Comput. Appl. Softw. 6, 330–333 (2014)Google Scholar
  14. 14.
    Luo, C., Guan, R., Wang, Z., et al.: HetPathMine: a novel transductive classification algorithm on heterogeneous information networks (2014)Google Scholar
  15. 15.
    Ji, M., Sun, Y., Danilevsky, M., Han, J., Gao, J.: Graph regularized transductive classification on heterogeneous information networks. In: Balcázar, J.L., Bonchi, F., Gionis, A., Sebag, M. (eds.) ECML PKDD 2010. LNCS (LNAI), vol. 6321, pp. 570–586. Springer, Heidelberg (2010). Scholar
  16. 16.
    Cunchao, T.U., Yang, C., Liu, Z., et al.: Network representation learning: an overview. Scientia Sinica (Informationis) 47, 980–996 (2017)Google Scholar
  17. 17.
    Ji, H., Shi, C., Wang, B.: Attention based meta path fusion for heterogeneous information network embedding. In: Geng, X., Kang, B.-H. (eds.) PRICAI 2018. LNCS (LNAI), vol. 11012, pp. 348–360. Springer, Cham (2018). Scholar
  18. 18.
    Cao, X., Zheng, Y., Shi, C., et al.: Meta-path-based link prediction in schema-rich heterogeneous information network. Int. J. Data Sci. Anal. 3(4), 285–296 (2017)CrossRefGoogle Scholar
  19. 19.
    Cen, Y., Jie, T., Zou, X., et al.: Representation learning for attributed multiplex heterogeneous network. In: The 25th ACM SIGKDD International Conference. ACM (2019)Google Scholar
  20. 20.
    Wang, X., et al.: Heterogeneous graph attention network. arXiv preprint arXiv:1903.07293 (2019)
  21. 21.
    Ara, L., Luo, X.: A data-driven network intrusion detection model based on host clustering and integrated learning: a case study on botnet detection. In: Wang, G., Feng, J., Bhuiyan, M.Z.A., Lu, R. (eds.) SpaCCS 2019. LNCS, vol. 11611, pp. 102–116. Springer, Cham (2019). Scholar
  22. 22.
    Montgomery, M., Chatterjee, P., Jenkins, J., Roy, K.: Touch analysis: an empirical evaluation of machine learning classification algorithms on touch data. In: Wang, G., Feng, J., Bhuiyan, M.Z.A., Lu, R. (eds.) SpaCCS 2019. LNCS, vol. 11611, pp. 147–156. Springer, Cham (2019). Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Hekai Zhang
    • 1
    • 2
  • Jibing Gong
    • 1
    • 2
    • 5
    Email author
  • Zhiyong Teng
    • 1
    • 2
  • Dan Wang
    • 1
    • 2
  • Hongfei Wang
    • 3
  • Linfeng Du
    • 4
  • Zakirul Alam Bhuiyan
    • 6
  1. 1.School of Information Science and EngineeringYanshan UniversityQinhuangdaoChina
  2. 2.The Key Lab for Computer Virtual Technology and System IntegrationYanshan UniversityQinhuangdaoChina
  3. 3.School of Computer Science and EngineeringBeihang UniversityBeijingChina
  4. 4.Shenyuan Honors CollegeBeihang UniversityBeijingChina
  5. 5.State Key Lab of Mathematical Engineering and Advanced ComputingWuxiChina
  6. 6.Department of Computer and Information SciencesFordham UniversityNew YorkUSA

Personalised recommendations