Mobile APP User Attribute Prediction by Heterogeneous Information Network Modeling
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.
Code (0)
등록된 구현이 없습니다.
Tasks
AttributeSimilar Papers 제목 키워드 기반
Predicting customer's gender and age depending on mobile phone data
In the age of data driven solution, the customer demographic attributes, such as gender and age, play a core role that may enable companies to enhance the offers of their services and target the right customer in the rig…
Gender PredictionManagementMarketingPredictionAn On-device Deep-Learning Approach for Attribute Extraction from Heterogeneous Unstructured Text
Mobile devices, with their rapidly growing usage, have turned into rich sources of user information, holding critical insights for betterment of user experience and personalization. Creating, receiving and storing import…
AttributeAttribute ExtractionMetapath-guided Heterogeneous Graph Neural Network for Intent Recommendation
With the prevalence of mobile e-commerce nowadays, a new type of recommendation services, called intent recommendation, is widely used in many mobile e-commerce Apps, such as Taobao and Amazon. Different from traditional…
AttributeFeature EngineeringGraph Neural NetworkA Semi-Supervised and Inductive Embedding Model for Churn Prediction of Large-Scale Mobile Games
Mobile gaming has emerged as a promising market with billion-dollar revenues. A variety of mobile game platforms and services have been developed around the world. One critical challenge for these platforms and services …
AttributeMetapaths guided Neighbors aggregated Network for?Heterogeneous Graph Reasoning
Most real-world datasets are inherently heterogeneous graphs, which involve a diversity of node and relation types. Heterogeneous graph embedding is to learn the structure and semantic information from the graph, and the…
DiversityGraph EmbeddingGraph Neural NetworkLink Prediction+2