基于图卷积神经网络的电子口碑病毒式传播热度预测研究

营销科学学报 ›› 2021, Vol. 1 ›› Issue (2): 60-75.

基于图卷积神经网络的电子口碑病毒式传播热度预测研究

徐志轩，钱明辉

徐志轩，首都经济贸易大学工商管理学院讲师，E-mail:zhixuan@cueb.edu.cn 钱明辉，通信作者，中国人民大学信息资源管理学院教授，E-mail:qmh@ruc.edu.cn

出版日期:2021-10-16 发布日期:2022-07-22
基金资助:
本文系中国人民大学科学研究基金(中央高校基本科研业务费专项资金资助)项目“社交网络品牌信息粘性影响因素与演化机制研究”(13XNI015)的研究成果。

Predicting eWOMs Popularity Based on Graph Convolutional Neural Networks

Xu Zhixuan，Qian Minghui

Xu Zhixuan，College of Management Administration, Capital University of Economics and Business; Qian Minghui，School of Information Resource Management, Renmin University of China

Online:2021-10-16 Published:2022-07-22

摘要/Abstract

摘要： 社交网络中频发的电子口碑病毒式传播现象在产品或服务营销以及品牌形象维护方面发挥着愈发重要的作用。如何有效预测电子口碑病毒式传播热度，对管理者制定合理的决策有重要意义。然而，由于现有方法的局限和口碑传播机制本身的复杂性，如何有效预测电子口碑的病毒式传播热度仍然是T具有挑战性的问题。为此，本研究从电子口碑传播规律出发，提出了一种新型的图卷积神经网络歐(eWOM-GCN)来有效预测电子口碑的病毒式传播热度。该方法釆用空域图卷积算法，无监督地提取电子口碑传播路径的鄆眄并且结合电子口碑传播的时间动态特征进行预测。本研究将站型应用于真实的电子口碑病毒式传播场景中,通过对比实验和消融实验有效验证了模型的性能。

关键词: 电子口碑, 病毒式传播, 热度预测, 图卷积神经网

Abstract: The frequent virus-like spread of eWOMs on social platforms plays an important role in product or service marketing and brand image maintenance. How to effectively predict the popularity of eWOMs is of great significance for managers to make reasonable decisions. However, due to the limitations of existing methods and the complexity of the eWOM transmission mechanism, predicting eWOM popularity is still a challenging problem. This study proposes an innovative graph convolutional neural network framework (eWOM-GCN) to predict the eWOM's popularity based on its propagation rules in social platforms. This model employs the spatial graph convolution algorithm to unsupervisedly extract the structural features of the eWOM's propagation paths, and combines its temporal features to predict the future popularity. In this study, the model was applied to the real virus-like spread of eWOMs, and the performance of the model was verified through comparative experiments and ablation experiments.

Key words: ?eWOMs , virus-like spread , popularity prediction , graph convolutional neural networks

徐志轩, 钱明辉. 基于图卷积神经网络的电子口碑病毒式传播热度预测研究[J]. 营销科学学报, 2021, 1(2): 60-75.

Xu Zhixuan, Qian Minghui . Predicting eWOMs Popularity Based on Graph Convolutional Neural Networks[J]. Journal of Marketing Science, 2021, 1(2): 60-75.