挖掘算法在海量数据环境下个性化网络营销策略中的应用

IF 2.1 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Journal of Intelligent Systems Pub Date : 2022-01-01 DOI:10.1515/jisys-2022-0014
Qianqian Pan, Gang Yang
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引用次数: 2

摘要

网络营销需要个性化的营销策略。本文研究了数据挖掘在个性化网络营销中的应用。在挖掘算法的基础上,设计了个性化营销方法。通过计算频繁封闭项集和正负样本支持数,得到营销成功率较高的区间。通过绩效分析发现,与传统营销方法相比,本文提出的营销方法的成功率提高了8%,并且在较小的间隔数和较小的最小成功数下具有更好的绩效。将所设计的方法应用于电信企业A后发现,采用本研究的营销方法后,A企业的营销成功率从2.72%提高到6.31%,表明了该方法的有效性。本研究的研究结果验证了数据挖掘算法在网络营销中的作用,有利于挖掘算法在个性化营销和商业模式创新中的进一步应用。
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Application of mining algorithm in personalized Internet marketing strategy in massive data environment
Abstract Internet marketing requires a personalized marketing strategy. In this study, the application of data mining in personalized Internet marketing was studied. Based on the mining algorithm, a personalized marketing method was designed. Through the calculation of frequent closed item sets and support counts of positive and negative samples, the interval with a high success rate for marketing was obtained. With performance analysis, it was found that the success rate of the marketing method proposed in this study improved 8% compared with the traditional marketing method and had a better performance under the smaller interval number and smaller minimum success number. After applying the designed method in telecommunication enterprise A, it was found that after adopting the marketing method of this study, the marketing success rate of enterprise A increased from 2.72 to 6.31%, which indicated the effectiveness of the method. The research results of this study verify the role of data mining algorithms in Internet marketing, which is conducive to the further application of mining algorithms in personalized marketing and innovation of business modes.
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来源期刊
Journal of Intelligent Systems
Journal of Intelligent Systems COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-
CiteScore
5.90
自引率
3.30%
发文量
77
审稿时长
51 weeks
期刊介绍: The Journal of Intelligent Systems aims to provide research and review papers, as well as Brief Communications at an interdisciplinary level, with the field of intelligent systems providing the focal point. This field includes areas like artificial intelligence, models and computational theories of human cognition, perception and motivation; brain models, artificial neural nets and neural computing. It covers contributions from the social, human and computer sciences to the analysis and application of information technology.
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