Identifying User Profile by Incorporating Self-Attention Mechanism based on CSDN Data Set  

作　　者：Junru Lu Le Chen Kongming Meng Fengyi Wang Jun Xiang Nuo Chen Xu Han Binyang Li 

机构地区：[1]School of Information Science and Technology,University of International Relations,Beijing 100091,China [2]Deep Brain Co.Ltd.,Shanghai 201200,China [3]University of Chinese Academy of Sciences,Beijing 100049,China [4]College of Information Engineering,Capital Normal University,Beijing 100048,China

出　　处：《Data Intelligence》2019年第2期160-175,共16页数据智能（英文）

基　　金：This work is partially supported by the National Natural Science Foundation of China(Grant numbers:61502115,61602326,U1636103 and U1536207);the Fundamental Research Fund for the Central Universities(Grant numbers:3262017T12,3262017T18,3262018T02 and 3262018T58).

摘　　要：With the popularity of social media,there has been an increasing interest in user profiling and its applications nowadays.This paper presents our system named UIR-SIST for User Profiling Technology Evaluation Campaign in SMP CUP 2017.UIR-SIST aims to complete three tasks,including keywords extraction from blogs,user interests labeling and user growth value prediction.To this end,we first extract keywords from a user’s blog,including the blog itself,blogs on the same topic and other blogs published by the same user.Then a unified neural network model is constructed based on a convolutional neural network(CNN)for user interests tagging.Finally,we adopt a stacking model for predicting user growth value.We eventually receive the sixth place with evaluation scores of 0.563,0.378 and 0.751 on the three tasks,respectively.

关 键 词：User profile Convolutional neural network(CNN) Self-attention Keyword extraction 

分 类 号：TP311.13[自动化与计算机技术—计算机软件与理论]