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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)

Abstract

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.

Keywords

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

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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

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