MMLUP: Multi-Source & Multi-Task Learning for User Profiles in Social Network

作     者：Dongjie Zhu Yuhua Wang Chuiju You Jinming Qiu Ning Cao Chenjing Gong Guohua Yang Helen Min Zhou 

作者机构：School of Computer Science and TechnologyHarbin Institute of TechnologyWeihai264209China College of Information EngineeringSanming University365004SanmingChina Fujian Province University Key Lab for Industry Big Data Analysis and ApplicationFujianChina College of Information EngineeringQingdao Binhai UniversityQingdaoChina Jiangsu Province Wireless Sensing System Application Engneering Technology Research and Development CentreChina School of EngineeringManukau Institute of TechnologyAuckland2241New Zealand 

出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)

年 卷 期：2019年第61卷第9期

页      面：1105-1115页

核心收录：

学科分类：0831[工学-生物医学工程（可授工学、理学、医学学位）] 0808[工学-电气工程] 0809[工学-电子科学与技术（可授工学、理学学位）] 08[工学] 0805[工学-材料科学与工程（可授工学、理学学位）] 0701[理学-数学] 0801[工学-力学（可授工学、理学学位）] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：This work is supported by State Grid Science and Technology Project under Grant No.520613180002,62061318C002 the Fundamental Research Funds for the Central Universities(Grant No.HIT.NSRIF.201714) Weihai Science and Technology Development Program(2016DXGJMS15) Key Research and Development Program in Shandong Provincial(2017GGX90103) Sanming Science and Technology Project,Grant No.2015-G-6,Shandong province vocational education educational reform research project.Grant No.2017209 Study and Development of Smart Agriculture Control System Based on Spark Big Data Decision(2017N0029) Jiangsu Province industrial Communication Technology Application Technology Innovation Team Project 

主　　题：User profiles multi-source multi-task learning social network 

摘      要：With the rapid development of the mobile Internet,users generate massive data in different forms in social network every day,and different characteristics of users are reflected by these social media *** to integrate multiple heterogeneous information and establish user profiles from multiple perspectives plays an important role in providing personalized services,marketing,and recommendation *** this paper,we propose Multi-source&Multi-task Learning for User Profiles in Social Network which integrates multiple social data sources and contains a multi-task learning framework to simultaneously predict various attributes of a ***,we design their own feature extraction models for multiple heterogeneous data ***,we design a shared layer to fuse multiple heterogeneous data sources as general shared representation for multi-task ***,we design each task’s own unique presentation layer for discriminant output of ***,we design a weighted loss function to improve the learning efficiency and prediction accuracy of each *** experimental results on more than 5000 Sina Weibo users demonstrate that our approach outperforms state-of-the-art baselines for inferring gender,age and region of social media users.