CAJ | 학술논문

提出了一种基于支持向量机的预测方法，通过分析智能手机应用的使用情况，预测用户的人口统计信息。手机使用行为数据约为5万智能手机用户在3个月期间使用手机应用产生的网络日志文件，包括179954181条日志记录。通过对日志记录的主题进行分析，可将179954181条日志记录匹配到266个不同的主题。在此基础上，通过将每个用户的人口统计信息与该用户对266个不同主题的访问权重进行关联，可构建训练数据，并代入支持向量机模型进行计算。实验结果表明该方法对用户的性别和年龄预测能够取得良好的预测结果。
제출료일충기우지지향량궤적예측방법，통과분석지능수궤응용적사용정황，예측용호적인구통계신식。수궤사용행위수거약위5만지능수궤용호재3개월기간사용수궤응용산생적망락일지문건，포괄179954181조일지기록。통과대일지기록적주제진행분석，가장179954181조일지기록필배도266개불동적주제。재차기출상，통과장매개용호적인구통계신식여해용호대266개불동주제적방문권중진행관련，가구건훈련수거，병대입지지향량궤모형진행계산。실험결과표명해방법대용호적성별화년령예측능구취득량호적예측결과。
A support-vector-machine-based predicting method is presented to predict users' demographic information by analyzing the usage of the applications in the smartphones. The smartphone usage data considered in this paper is a network log file, which records smartphone applications usage of 50 000 smartphone users for three months, including 179 954 181 entries. By analyzing the topic of each entry, the 179 954 181 entries can be matched with 266 distinct topics. Based on this result, by correlating the users’ demographic information with their query weight of such 266 distinct topics, a training data can be constructed and imported to support vector machine model for computation. The results of experiments show that the method proposed in this paper can well predict uses’ gender and age.