Mobile APP User Attribute Prediction by Heterogeneous Information Network Modeling

TL;DR

This paper proposes the HetPathMine and TPathMine models for predicting user attributes like age and gender using heterogeneous information networks, achieving higher accuracy than traditional methods by incorporating user click behavior and meta-path optimization.

Contribution

The paper introduces novel models that leverage heterogeneous information networks and click-based user preferences for improved user attribute prediction.

Findings

Higher accuracy in user attribute prediction compared to traditional methods

TPathMine effectively classifies users by age groups

Meta-path weights align with human intuition and real-world data

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. 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.