基于机器学习的本地生活O2O顾客满意度预测研究
首发时间:2021-03-31
摘要:在技术的飞速发展和国家利好政策的推动下,各行各业都与互联网产生了越来越深度的融合和紧密的连接,本地生活O2O应运而生,市场规模得以迅速发展与扩张,但同时平台同质化竞争激烈、流量渠道缺乏创新、数据价值挖掘不足等问题也限制着市场持续向好发展。本文聚焦于本地生活O2O模式下的顾客满意度研究,通过对头部企业"口碑"平台的真实交易数据进行挖掘分析,运用机器学习理论搭建起顾客满意度预测模型,并对不同算法的预测结果进行对比分析,通过特征提取等方法提高模型预测性能。
关键词: 计算机应用 本地生活O2O 顾客满意度预测 特征工程 机器学习
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Research on customer satisfaction predication of O2O local service based on machine learning
Abstract:Driven by the rapid development of technology and favorable national policies, all walks of life have a closer connection with the Internet. The O2O local service came into being, and the market scale was able to develop and expand rapidly. Meanwhile, issues such as fierce competition for platform homogenization, lack of innovation in traffic channels, and insufficient data value mining have also restricted the continued development of the market. This study focuses on the customer satisfaction of O2O local service. By mining and analyzing the real transaction data of the leading company "Koubei", this paper uses machine learning theory to build a customer satisfaction prediction model, and compares the results of different algorithms, improves model prediction Research on customer satisfaction predication of O2O local service based on machine learningperformance through methods such as feature extraction .
Keywords: computer applications O2O local service customer satisfaction prediction feature engineering machine learning
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