颜波，张磊，褚学宁

上海交通大学 机械与动力工程学院， 上海 200240

出版日期:2019-07-28发布日期:2019-08-02
通讯作者:褚学宁，男，教授，博士生导师；E-mail: xnchu@sjtu.edu.cn.
作者简介:颜波（1993-），男，云南省保山市人，硕士生，主要研究方向为智能化与数字化设计.
基金资助:国家自然科学基金资助项目（51875345，51475290，51075261）

User Experience Evaluation Modeling Based on Convolutional Neural Network

YAN Bo,ZHANG Lei,CHU Xuening

School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China

Online:2019-07-28Published:2019-08-02

摘要/Abstract

摘要： 提出了一种基于卷积神经网络的用户感知评估建模方法，充分利用产品使用数据来定量地建立用户感知评估和产品性能参数之间的映射关系，以支持产品设计改进.首先，利用滑动窗口技术将时间序列形式的使用数据转换为一系列数据单元，并在此基础上建立适用于用户感知评估模型的卷积神经网络结构；然后，通过K-折交叉验证分析确定模型的最优超参数并改善模型的过拟合问题；最后，以智能手机用户感知建模为例验证了方法的有效性.结果表明，所提出的方法能够自动从使用数据中提取出有效特征，用于用户感知评估预测，减少了建模过程中对用户和设计师的依赖，可以帮助设计师及时准确地评估产品表现，为产品设计改进提供决策支持.
关键词: 产品设计； 用户感知； 使用数据； 卷积神经网络； 特征提取
Abstract: In order to transform the usage data into appropriate information that can improve the products through design modification, a method based on convolution neural network is proposed for user experience evaluation modeling, which can make full use of the usage data to establish the mapping relationship between the user information and the product engineering requirements. Firstly, the time-series usage data was converted into a series of data units by sliding window technique, and a convolution neural network architecture suitable for user experience evaluation model was established. Then, the optimal hyper parameters was selected and the over fitting problem of the model was improved by K-fold cross validation analysis. Finally, the validity of the proposed method was demonstrated by a case study of smart phone user experience evaluation modeling. The results indicated that the proposed method can automatically extract effective features from raw usage data, which can used for user experience evaluation prediction. Thus, the proposed method can decrease the dependence of the users and designers when modeling, which can help designers to assess the product performance in real time and accurately and provide support information for design decisions through usage data.
Key words: product design; user experience; usage data; convolutional neural network; feature extracting


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