A Website Source Evaluation Algorithm Based on Comprehensive Feature Analysis

2019 IEEE International Conferences on Ubiquitous Computing & Communications (IUCC) and Data Science and Computational Intelligence (DSCI) and Smart Computing, Networking and Services (SmartCNS) Pub Date : 2019-10-01 DOI:10.1109/IUCC/DSCI/SmartCNS.2019.00082

Baosheng Yin, Longlong Zhang, D. Pei, Yusheng Yan

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引用次数: 1

Abstract

Traditional web page sorting algorithms can only find the single web page that is the most relevant to keywords, but can not find the relevant website information source. For tackling the problem, we Propose a website information source evaluation algorithm based on comprehensive feature analysis. This algorithm first obtains multiple web pages corresponding to keywords through Baidu and other search engines, then obtains the contents of corresponding website information sources through crawler program and extracts the features, and finally obtains the sorting results of information sources of relevant websites by calculating relevancy combining BM25 algorithm and cosine distance. At the same time, combined with the implicit feedback behavior of users' browsing time, the sorting results could be dynamically adjusted to make the search results personalized. Experiment results show that this approach could make full use of web features, and improve the quality of web source evaluation algorithm by combining the semantic information of web content.

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基于综合特征分析的网站资源评价算法

传统的网页排序算法只能找到与关键词最相关的单个网页，而无法找到相关的网站信息源。针对这一问题，我们提出了一种基于综合特征分析的网站信息源评价算法。该算法首先通过百度等搜索引擎获取关键词对应的多个网页，然后通过爬虫程序获取相应网站信息源的内容并提取特征，最后结合BM25算法和余弦距离计算相关度，得到相关网站信息源的排序结果。同时，结合用户浏览时间的隐式反馈行为，可以动态调整排序结果，使搜索结果个性化。实验结果表明，该方法可以充分利用web特征，结合web内容的语义信息，提高web资源评价算法的质量。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

2019 IEEE International Conferences on Ubiquitous Computing & Communications (IUCC) and Data Science and Computational Intelligence (DSCI) and Smart Computing, Networking and Services (SmartCNS)

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