  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.
