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浙江大学学报(工学版)
浙江大学学报(工学版)  2021, Vol. 55 Issue (4): 608-614    DOI: 10.3785/j.issn.1008-973X.2021.04.002
计算机技术、电信技术     
基于行为感知的用户画像技术
尤明辉(),殷亚凤*(),谢磊,陆桑璐
南京大学 计算机软件新技术国家重点实验室，江苏 南京 210023
User profiling based on activity sensing
Ming-hui YOU(),Ya-feng YIN*(),Lei XIE,Sang-lu LU
State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210023, China
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摘要：

为了在保护用户隐私的同时构建用户画像，提出基于行为感知的用户画像技术. 采用智能手机内置的惯性传感器，可以感知识别用户使用手机的行为（如浏览阅读、打字聊天、通话等）. 通过识别的用户行为挖掘用户特性，如性别、社交性格、压力状态等，构建初步的用户画像. 实验结果表明，利用该方法能够较好地识别用户行为并构建用户画像，其中行为识别的准确率为87.2%，针对性别、社交性格、压力状态3个具体用户特性的预测准确率分别为81.8%、72.7%、72.7%.
关键词： 智能手机;  惯性传感器;  行为感知;  用户特性挖掘;  用户画像    
Abstract:

A user profiling method based on activity sensing was proposed in order to build user profile while protecting their privacy. The built-in inertial sensors of the smart phone were used to sense the user activities (e.g., browsing, typing, calling etc.). Then the recognized activities were used to mine user characteristics, such as social personality and stress level, in order to build a preliminary user profile. The experimental results show that the method can recognize user activities and build user profiles. The accuracy of activity recognition was 87.2%, while the accuracy of predictions for the three user characteristics in gender, social personality, and stress level was 81.8%, 72.7%, 72.7%, respectively.
Key words: smartphone    inertial sensor    activity sensing    user characteristic mining    user profiling
收稿日期: 2021-01-22 出版日期: 2021-05-07
CLC:  TP 399  
基金资助: 国家自然科学基金资助项目（61802169，61872174，61832008，61902175，61906085）；江苏省自然科学基金资助项目（BK20180325，BK20190293）；江苏省重点研发基金资助项目（BE2018116）；软件新技术与产业化协同创新中心资助项目
通讯作者: 殷亚凤     E-mail: mf20330111@smail.nju.edu.cn;yafeng@nju.edu.cn
作者简介: 尤明辉（1998—），男，硕士生，从事智能感知的研究. orcid.org/0000-0003-3493-1121. E-mail： mf20330111@smail.nju.edu.cn
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引用本文:

尤明辉,殷亚凤,谢磊,陆桑璐. 基于行为感知的用户画像技术[J]. 浙江大学学报(工学版), 2021, 55(4): 608-614.

Ming-hui YOU,Ya-feng YIN,Lei XIE,Sang-lu LU. User profiling based on activity sensing. Journal of ZheJiang University (Engineering Science), 2021, 55(4): 608-614.

链接本文:

http://www.zjujournals.com/eng/CN/10.3785/j.issn.1008-973X.2021.04.002    或    http://www.zjujournals.com/eng/CN/Y2021/V55/I4/608

图 1  基于行为感知实现用户画像模型框架
图 2  使用卡尔曼滤波降噪
图 3  对时序数据进行数据分割
时域特征 计算公式
均值 $\mu =\dfrac{1}{N}{\displaystyle\sum }_{i=1}^{{N} }{C}_{i}$
标准差 $\sigma =\sqrt{\dfrac{1}{N}{\displaystyle\sum }_{i=1}^{N}{\left({C}_{i}-\mu\right)}^{2} }$
最大值 $f_{{\rm{max}}}={\rm{max} }\;\left\{ {C}_{i}|i\in \left(1, N\right)\right\}$
最小值 $f_{{\rm{min}}}={\rm{min} }\;\left\{ {C}_{i}|i\in \left(1, N\right)\right\}$
过均值率 $f_{{\rm{above}}}=\dfrac{ {\rm{num} }\;\left\{ {C}_{i}|i\in \left(1, N\right),{C}_{i} > \mu\right\}}{N}$
表 1  所选择的时域特征
频域特征 计算公式
均值 ${\mu }_{{\rm{amp}}}=\dfrac{1}{N}{\displaystyle\sum }_{i=1}^{{N} }{G}_{i}$
标准差 ${\sigma }_{{\rm{amp}}}=\sqrt{\dfrac{1}{N}{\displaystyle\sum }_{i=1}^{N}{\left({G}_{i}-{\mu }_{{\rm{amp}}}\right)}^{2} }$
峰度 ${\gamma }_{{\rm{amp}}}=\dfrac{1}{N}{\displaystyle\sum }_{i=1}^{N}{\left[\dfrac{ {G}_{i}-{\mu }_{{\rm{amp}}} }{ {\sigma }_{{\rm{amp}}} }\right]}^{4}- 3$
表 2  所选择的频域特征
图 4  特征向量的具体形式
图 5  用户画像内容与用户标签形式
行为类别 浏览阅读 游戏 打字聊天 通话 视频
浏览阅读 77.6% 0.0% 16.5% 2.0% 3.9%
游戏 1.5% 89.9% 2.0% 0.3% 6.3%
打字聊天 5.2% 4.0% 87.9% 2.4% 0.5%
通话 4.8% 0.0% 5.1% 88.7% 1.4%
视频 3.7% 3.5% 0.2% 0.2% 92.4%
表 3  行为感知预测正确率的混淆矩阵
图 6  不同用户在行为感知上的正确率
用户特性 Acc /%
性别 81.8
社交性格 72.7
压力状态 72.7
表 4  对用户特性分类的准确率
图 7  不同数据收集时间下对用户特性预测正确率
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