搜索引擎优化
审计
Python(编程语言)
采购
计算机科学
万维网
软件
铲运机现场
Boosting(机器学习)
排名(信息检索)
业务
搜索引擎
营销
情报检索
机器学习
会计
程序设计语言
操作系统
作者
Konstantinos I. Roumeliotis,Nikolaos D. Tselikas
标识
DOI:10.3390/informatics10030068
摘要
In the present-day digital landscape, websites have increasingly relied on digital marketing practices, notably search engine optimization (SEO), as a vital component in promoting sustainable growth. The traffic a website receives directly determines its development and success. As such, website owners frequently engage the services of SEO experts to enhance their website’s visibility and increase traffic. These specialists employ premium SEO audit tools that crawl the website’s source code to identify structural changes necessary to comply with specific ranking criteria, commonly called SEO factors. Working collaboratively with developers, SEO specialists implement technical changes to the source code and await the results. The cost of purchasing premium SEO audit tools or hiring an SEO specialist typically ranges in the thousands of dollars per year. Against this backdrop, this research endeavors to provide an open-source Python-based Machine Learning SEO software tool to the general public, catering to the needs of both website owners and SEO specialists. The tool analyzes the top-ranking websites for a given search term, assessing their on-page and off-page SEO strategies, and provides recommendations to enhance a website’s performance to surpass its competition. The tool yields remarkable results, boosting average daily organic traffic from 10 to 143 visitors.
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