[1]文继权.基于相关性模型与大数据分析的关键词优化方法研究[J].计算机技术与发展,2024,34(04):30-34.[doi:10. 3969 / j. issn. 1673-629X. 2024. 04. 005]
　WEN Ji-quan.Research on Keyword Optimization Method Based on Correlation Model and Big Data Analysis[J].,2024,34(04):30-34.[doi:10. 3969 / j. issn. 1673-629X. 2024. 04. 005]
点击复制

基于相关性模型与大数据分析的关键词优化方法研究()
分享到：

《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
34
期数:
2024年04期
页码:
30-34
栏目:
大数据与云计算
出版日期:
2024-04-10

文章信息/Info

Title:
Research on Keyword Optimization Method Based on Correlation Model and Big Data Analysis
文章编号:
1673-629X(2024)04-0030-05
作者:
文继权
大连海洋大学 应用技术学院,辽宁 大连 116300
Author(s):
WEN Ji-quan
School of Applied Technology,Dalian Ocean University,Dalian 116300,China
关键词:
相关性模型; 大数据分析; 电商运营; 关键词优化; 点击率
Keywords:
relational model; big data analysis; e-commerce operation; keyword optimization; click through rate
分类号:
TP391. 9;F724. 6;N945. 12
DOI:
10. 3969 / j. issn. 1673-629X. 2024. 04. 005
摘要:
随着计算机网络与信息技术的快速发展,电子商务得到了爆发式的发展,电子商务网店运营成为广大电商从业者所追捧的技术。 随着大数据技术的普及应用,电子商务网店运营技术也在不断地推陈出新,基于大数据的关键词筛选优化就是人们所关注的重要技术。 相关性模型是电商平台商品搜索排序的基础模型,基于相关性模型的搜索引擎优化(SEO) 是目前电子商务数据化运营中进行关键词优化筛选的基本方法。 该文将原有方法与大数据分析相结合,从关键词采集、筛选、组合、监测、调换等多维度进行关键词的优化筛选,提出并论述关键词设置的三个筛选优化模型:竞争系数、单品位竞争个数、关键词主体价格区间适应性,并对筛选优化后选用的关键词进行了应用有效性实践监测。 大量监测数据表明,使用相关性模型与大数据分析相结合的关键词筛选优化方法得到的商品标题关键词,可以有效提升商品的曝光量、点击率与转化率,实现更高效的网店引流与成交量。
Abstract:
With the rapid development of computer networks and information technology, e - commerce has experienced explosivedevelopment. E-commerce online store operation?
has become a technology pursued by a large number of e - commerce practitioners.With the popularization and application of big data technology, e - commerce online?
store operation technology is also constantlyevolving,and keyword selection and optimization based on big data is an important technology that people pay attention to. The　correlation model is the basic model for sorting product searches on e-commerce platforms. Search Engine Optimization ( SEO) basedon the correlation model is currently?
the basic method for keyword optimization and screening in e-commerce data-driven operations.We combine the original method with big data analysis to optimize and screen keywords from multiple dimensions such as keyword collection,screening,combination,monitoring,and exchange,and propose and discuss three optimization models for keyword selection:competition coefficient,number of single grade competitions,and adaptability of keyword subject price range. Conduct practical monitoringon the effectiveness of the selected keywords after selection optimization. A large amount of monitoring data shows that using thekeyword selection optimization method combining correlation model and?
big data analysis to obtain product title keywords can effectivelyimprove the exposure of the product click through rate and conversion rate,achieving more efficient online store traffic and transactionvolume.

相似文献/References:

[1]金宗泽,冯亚丽,文必龙,等. 大数据分析流程框架的研究[J].计算机技术与发展,2014,24(08):117.
　JIN Zong-ze,FENG Ya-l,WEN Bi-long,et al. Research on Framework of Big Data Analytic Process[J].,2014,24(04):117.
[2]苗立志,刁继尧,娄 冲,等.基于 Spark 和随机森林的乳腺癌风险预测分析[J].计算机技术与发展,2019,29(08):142.[doi:10. 3969 / j. issn. 1673-629X. 2019. 08. 027]
　MIAO Li-zhi,DIAO Ji-yao,LOU Chong,et al.Breast Cancer Risk Prediction Analysis Based on Apache Spark and Random Forest Algorithm[J].,2019,29(04):142.[doi:10. 3969 / j. issn. 1673-629X. 2019. 08. 027]
[3]陆兴华,林佳聪,谢欣殷,等.基于大数据的物联网用户行为模式挖掘[J].计算机技术与发展,2019,29(12):99.[doi:10. 3969 / j. issn. 1673-629X. 2019. 12. 018]
　LU Xing-hua,LIN Jia-cong,XIE Xin-yin,et al.Mining of User Behavior Pattern in Internet of Things Based on Big Data[J].,2019,29(04):99.[doi:10. 3969 / j. issn. 1673-629X. 2019. 12. 018]
[4]赵 洋.基于大数据的神经网络算法分析与研究[J].计算机技术与发展,2021,31(增刊):52.[doi:10. 3969 / j. issn. 1673-629X. 2021. S. 010]
　ZHAO Yang.Analysis and Research of Neural Network Algorithm Based on Big Data[J].,2021,31(04):52.[doi:10. 3969 / j. issn. 1673-629X. 2021. S. 010]

更新日期/Last Update: 2024-04-10