论文标题
基于Hadoop和R的搜索引擎自动搜索性能的线性回归评估
Linear Regression Evaluation of Search Engine Automatic Search Performance Based on Hadoop and R
论文作者
论文摘要
搜索引擎的自动搜索性能已成为衡量用户体验差异的重要组成部分。有效的自动搜索系统可以显着改善搜索引擎的性能并增加用户流量。 Hadoop具有强大的数据集成和分析功能，而R在线性回归中具有出色的统计能力。本文将提出基于Hadoop和R的线性回归，以量化自动检索系统的效率。我们使用R的功能属性来在线性相关性时转换用户的搜索结果。这样，最终输出结果具有多个显示表单，而不是网页预览接口。本文提供了可行的解决方案，解决了当前搜索引擎算法的缺点，该搜索引擎算法缺乏一次或两次搜索精度和多种类型的搜索结果。我们可以使用公共数据集对用户需求进行个性化的回归分析，并优化资源集成，以获取大多数相关信息。
The automatic search performance of search engines has become an essential part of measuring the difference in user experience. An efficient automatic search system can significantly improve the performance of search engines and increase user traffic. Hadoop has strong data integration and analysis capabilities, while R has excellent statistical capabilities in linear regression. This article will propose a linear regression based on Hadoop and R to quantify the efficiency of the automatic retrieval system. We use R's functional properties to transform the user's search results upon linear correlations. In this way, the final output results have multiple display forms instead of web page preview interfaces. This article provides feasible solutions to the drawbacks of current search engine algorithms lacking once or twice search accuracies and multiple types of search results. We can conduct personalized regression analysis for user's needs with public datasets and optimize resources integration for most relevant information.
