计算机应用 ›› 2019, Vol. 39 ›› Issue (7): 1888-1893.DOI: 10.11772/j.issn.1001-9081.2019010061

• 人工智能 • 上一篇    下一篇

消费者偏好预测的深度学习神经网络模型

金忠星1,2, 李东1   

  1. 1. 哈尔滨工业大学 经济与管理学院, 哈尔滨 150001;
    2. 金策工业综合大学 自动化学院, 平壤 950003, 朝鲜民主主义人民共和国
  • 收稿日期:2019-01-10 修回日期:2019-02-20 发布日期:2019-04-15 出版日期:2019-07-10
  • 通讯作者: 李东
  • 作者简介:金忠星(1984-),男,朝鲜平壤人,博士研究生,主要研究方向:深度学习、脑机接口;李东(1958-),男,黑龙江哈尔滨人,教授,博士,主要研究方向:企业经济学、人力资源管理、市场营销。
  • 基金资助:

    国家自然科学基金资助项目(61301012,61401117,61471140)。

Deep learning neural network model for consumer preference prediction

KIM Chungsong<sup>1,2</sup>, LI Dong<sup>1</sup>   

  1. 1. School of Management, Harbin Institute of Technology, Harbin Heilongjiang 150001, China;
    2. School of Automation, Kimchaek University of Technology, Pyongyang 950003, Democratic People's Republic of Korea
  • Received:2019-01-10 Revised:2019-02-20 Online:2019-04-15 Published:2019-07-10
  • Supported by:

    This work is partially supported by the National Natural Science Foundation of China (61301012, 61401117, 61471140).

摘要:

通过对于人类大脑活动的研究来分析消费者对广告和产品的反应的神经营销正在受到新的关注。针对基于脑电波(EEG)的神经营销,提出了一种基于深度学习神经网络的消费者对产品的偏好预测方法。首先,为了提取消费者EEG的特征,采用短时傅里叶变换(STFT)与双调和样条插值,从多通道脑电信号中得到了5个不同频带的EEG形图视频;然后,提出了一种结合5个三维卷积神经网络(3D CNN)与多层长短期记忆(LSTM)神经网络的预测模型,用于从脑电地形图视频预测到消费者的偏好。与卷积神经网络(CNN)模型和LSTM神经网络模型相比,消费者依赖模型的平均准确度分别提高了15.05个百分点和19.44个百分点,消费者独立模型的平均准确度分别提高了16.34个百分点和17.88个百分点。理论分析与实验结果表明,所提出的消费者偏好预测系统可以以低成本提供有效的营销策略开发和营销管理。

关键词: 深度学习, 三维卷积神经网络, 长短期记忆神经网络, 神经营销, 脑电地形图视频

Abstract:

Neuromarketing, by which consumer responses to advertisements and products are analyzed through research on human brain activity, is receiving new attention. Aiming at neuromarketing based on ElectroEncephaloGraphy (EEG), a method of consumer preference prediction based on deep learning neural network was proposed. Firstly, in order to extract features of consumer's EEG, five different frequency bands of EEG topographic videos were obtained from multi-channel EEG signals by using Short Time Fourier Transform (STFT) and biharmonic spline interpolation. Then, a prediction model combining five three-Dimensional Convolutional Neural Networks (3D CNNs) and multi-layer Long Short-Term Memory (LSTM) neural networks was proposed for predicting consumer preference from EEG topographic videos. Compared with the Convolutional Neural Network (CNN) model and LSTM neural network model, the average accuracy of consumer-dependence model was increased by 15.05 percentage points and 19.44 percentage points respectively, and the average accuracy of consumer-independence model was increased by 16.34 percentage points and 17.88 percentage points respectively. Theoretical analysis and experimental results show that the proposed consumer preference prediction system can provide effective marketing strategy development and marketing management at low cost.

Key words: deep learning, 3D Convolutional Neural Network (3D CNN), Long Short-Term Memory (LSTM) neural network, neuromarketing, ElectroEncephaloGraphy (EEG) topographic video

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