Weighted PageRank Algorithm Search Engine Ranking Model for Web Pages

As data grows in size,search engines face new challenges in extracting more relevant content for users’searches.As a result,a number of retrieval and ranking algorithms have been employed to ensure that the results are relevant to the user’s requirements.Unfortunately,most existing indexes and ranking algo-rithms crawl documents and web pages based on a limited set of criteria designed to meet user expectations,making it impossible to deliver exceptionally accurate results.As a result,this study investigates and analyses how search engines work,as well as the elements that contribute to higher ranks.This paper addresses the issue of bias by proposing a new ranking algorithm based on the PageRank(PR)algorithm,which is one of the most widely used page ranking algorithms We pro-pose weighted PageRank(WPR)algorithms to test the relationship between these various measures.The Weighted Page Rank(WPR)model was used in three dis-tinct trials to compare the rankings of documents and pages based on one or more user preferences criteria.Thefindings of utilizing the Weighted Page Rank model showed that using multiple criteria to rankfinal pages is better than using only one,and that some criteria had a greater impact on ranking results than others.

Ranking of Web Pages in a Personalized Search

The basic idea behind a personalized web search is to deliver search results that are tailored to meet user needs, which is one of the growing concepts in web technologies. The personalized web search presented in this paper is based on exploiting the implicit feedbacks of user satisfaction during her web browsing history to construct a user profile storing the web pages the user is highly interested in. A weight is assigned to each page stored in the user’s profile;this weight reflects the user’s interest in this page. We name this weight the relative rank of the page, since it depends on the user issuing the query. Therefore, the ranking algorithm provided in this paper is based on the principle that;the rank assigned to a page is the addition of two rank values R_rank and A_rank. A_rank is an absolute rank, since it is fixed for all users issuing the same query, it only depends on the link structures of the web and on the keywords of the query. Thus, it could be calculated by the PageRank algorithm suggested by Brin and Page in 1998 and used by the google search engine. While, R_rank is the relative rank, it is calculated by the methods given in this paper which depends mainly on recording implicit measures of user satisfaction during her previous browsing history.

Personalized web pages ranking algorithm based on user preferences

In order to rank searching results according to the user preferences,a new personalized web pages ranking algorithm called PWPR（personalized web page ranking）with the idea of adjusting the ranking scores of web pages in accordance with user preferences is proposed.PWPR assigns the initial weights based on user interests and creates the virtual links and hubs according to user interests.By measuring user click streams,PWPR incrementally reflects users’ favors for the personalized ranking.To improve the accuracy of ranking, PWPR also takes collaborative filtering into consideration when the query with similar is submitted by users who have similar user interests. Detailed simulation results and comparison with other algorithms prove that the proposed PWPR can adaptively provide personalized ranking and truly relevant information to user preferences.

Web Page Watermarking for Tamper-Proof

This paper proposed a watermarking algorithm for tamper-proof of web pages. For a web page, it generates a watermark consisting of a sequence of Space and Tab. The wa termark is then embedded into the web page after each word and each line. When a watermarked web page is tampered, the extracted watermark can detect and locate the modifications to the web page. Besides, the framework of watermarked Web Server system was given. Compared with traditional digital signature methods, this watermarking method is more transparent in that there is no necessary to detach the watermark before displaying web pages. The e xperimental results show that the proposed scheme is an effective tool for tamper-proof of web pages.

Web Page Forensics:A Web Spider Based Approach

Cyber-crimes are growing rapidly,so it is important to obtain the digital evidence on the web page.Usually,people can examine the browser history on the client side and data files on the server side,but both of them have shortcomings in real criminal investigation.To overcome the weakness,this paper designs a web page forensic scheme to snapshot the pages from web servers with the help of web spider.Also,it designs several steps to improve the trustworthiness of these pages.All the pages will be dumped in local database which can be presented as reliable evidence on the court.

Web Page Translation of Garment Enterprises in Zhejiang： Mass Communication Perspective

As part of business translation, web page translation is an effective way of global communication. Accurate and adequate translation of web pages can enhance the enterprises＇ competitiveness. This paper focuses on the features of Chinese and Western web pages of garment enterprises and the translation strategy with the guidance of mass communication theory. Results illustrate that Chinese texts favor symmetrical phrases rich in cultural connotation, whereas English texts prefer plain language with fewer culture-loaded expressions and the latter focuses more on the feeling of audiences. As a result, audience-oriented translation strategy is strongly recommended so as to maximize the communication effect of the enterprise web pages

Statistical Models for Web Pages Usability

The usability of an interface is a fundamental issue to elucidate. Many researchers argued that many usability results and recommendations lack empirical and experimental data. In this research, the usability of the web pages is evaluated using several carefully selected statistical models. Universities web pages are chosen as subjects for this work for ease of comparison and ease of collecting data. A series of experiments has been conducted to investigate into the usability and design of the universities web pages. Prototype web pages have been developed according to the structured methodologies of web pages design and usability. Universities web pages were evaluated together with the prototype web pages using a questionnaire which was designed according to the Human Computer Interactions (HCI) heuristics. Nine (users) respondents’ variables and 14 web pages variables (items) were studied. Stringent statistical analysis was adopted to extract the required information to form the data acquired, and augmented interpretation of the statistical results was followed. The results showed that the analysis of variance (ANOVA) procedure showed there were significant differences among the universities web pages regarding most of the 23 items studied. Duncan Multiple Range Test (DMRT) showed that the prototype usability performed significantly better regarding most of the items. The correlation analysis showed significant positive and negative correlations between many items. The regression analysis revealed that the most significant factors (items) that contributed to the best model of the universities web pages design and usability were: multimedia in the web pages, the web pages icons (alone) organisation and design, and graphics attractiveness. The results showed some of the limitations of some heuristics used in conventional interface systems design and proposed some additional heuristics in web pages design and usability.

Research of Web Pages Categorization

In this paper, we discuss several issues related to automated classification of web pages, especially text classification of web pages. We analyze features selection and categorization algorithms of web pages and give some suggestions for web pages categorization.

Improved Term Weighting Technique for Automatic Web Page Classification

Automatic web page classification has become inevitable for web directories due to the multitude of web pages in the World Wide Web. In this paper an improved Term Weighting technique is proposed for automatic and effective classification of web pages. The web documents are represented as set of features. The proposed method selects and extracts the most prominent features reducing the high dimensionality problem of classifier. The proper selection of features among the large set improves the performance of the classifier. The proposed algorithm is implemented and tested on a benchmarked dataset. The results show the better performance than most of the existing term weighting techniques.

Research on Web Page Automatic Classification Based on Internet News Corpus

Web pages contain more abundant contents than pure text ,such as hyperlinks,html tags and metadata et al.So that Web page categorization is different from pure text. According to Internet Chinese news pages, a practical algorithm for extracting subject concepts from web page without thesaurus was proposed, when incorporated these category-subject concepts into knowledge base, Web pages was classified by hybrid algorithm, with experiment corpus extracting from Xinhua net. Experimental result shows that the categorization performance is improved using Web page feature.

Exploration of the Ideological and Political Education in the“Web Design and Website Construction”Course

“Web Design and Website Construction”is a core professional course for e-commerce majors.This article explores how to integrate ideological and political education into the curriculum teaching of the course from three aspects:the necessity of ideological and political education construction,the construction goals,and the implementation paths.It not only improves students’professional and technical skills,but also guides students to establish a correct outlook on life and values,and cultivates students’comprehensive development of comprehensive literacy.

Web page classification based on heterogeneous features and a combination of multiple classifiers

Precise web page classification can be achieved by evaluating features of web pages, and the structural features of web pages are effective complements to their textual features. Various classifiers have different characteristics, and multiple classifiers can be combined to allow classifiers to complement one another. In this study, a web page classification method based on heterogeneous features and a combination of multiple classifiers is proposed. Different from computing the frequency of HTML tags, we exploit the tree-like structure of HTML tags to characterize the structural features of a web page. Heterogeneous textual features and the proposed tree-like structural features are converted into vectors and fused. Confidence is proposed here as a criterion to compare the classification results of different classifiers by calculating the classification accuracy of a set of samples. Multiple classifiers are combined based on confidence with different decision strategies, such as voting, confidence comparison, and direct output, to give the final classification results. Experimental results demonstrate that on the Amazon dataset, 7-web-genres dataset, and DMOZ dataset, the accuracies are increased to 94.2%, 95.4%, and 95.7%, respectively. The fusion of the textual features with the proposed structural features is a comprehensive approach, and the accuracy is higher than that when using only textual features. At the same time, the accuracy of the web page classification is improved by combining multiple classifiers, and is higher than those of the related web page classification algorithms.

基于PageRank的Web挖掘改进算法

针对Google使用的PageRank算法,提出一种改进的Web挖掘算法。实现该算法时,将网页使用信息和网页添加日期信息做成点击向量和日期向量,2个向量加权后标准化得到的一个向量作为常数加入到改进的迭代算法。实验结果证明,改进算法可以提高网页重要性判断的准确度。

A Novel Feature Selection Framework for Automatic Web Page Classification

The number of Internet users and the number of web pages being added to www increase dramatically every day. It is therefore required to automatically and efficiently classify web pages into web directories. This helps the search engines to provide users with relevant and quick retrieval results. As web pages are represented by thousands of features, feature selection helps the web page classifiers to resolve this large scale dimensionality problem. This paper proposes a new feature selection method using Ward’s minimum variance measure. This measure is first used to identify clusters of redundant features in a web page. In each cluster, the best representative features are retained and the others are eliminated. Removing such redundant features helps in minimizing the resource utilization during classification. The proposed method of feature selection is compared with other common feature selection methods. Experiments done on a benchmark data set, namely WebKB show that the proposed method performs better than most of the other feature selection methods in terms of reducing the number of features and the classifier modeling time.

PageCluster:一种Web页面层次聚类方法

提出了Web页面聚类算法PageCluster及相应的改进算法ImPageCluster。该方法在兼顾Web站点结构和页面链接的同时,基于各个页面的重要程度对各个超链接进行赋权。与传统聚类算法相比,该算法不需要事先给定相似度阈值。实验结果证实了该算法的可行性和高效性。

Measuring web page complexity by analyzing TCP flows and HTTP headers

To understand website complexity deeply, a web page complexity measurement system is developed. The system measures the complexity of a web page at two levels： transport-level and content-level, using a packet trace-based approach rather than server or client logs. Packet traces surpass others in the amount of information contained. Quantitative analyses show that different categories of web pages have different complexity characteristics. Experimental results show that a news web page usually loads much more elements at more accessing levels from much more web servers within diverse administrative domains over much more concurrent transmission control protocol （TCP） flows. About more than half of education pages each only involve a few logical servers, where most of elements of a web page are fetched only from one or two logical servers. The number of content types for web game traffic after login is usually least. The system can help web page designers to design more efficient web pages, and help researchers or Internet users to know communication details.

Research on Web Page Classification Method Based on Query Log

Web page classification is an important application in many fields of Internet information retrieval,such as providing directory classification and vertical search. Methods based on query log which is a light weight version of Web page classification can avoid Web content crawling, making it relatively high in efficiency, but the sparsity of user click data makes it difficult to be used directly for constructing a classifier. To solve this problem, we explore the semantic relations among different queries through word embedding, and propose three improved graph structure classification algorithms. To reflect the semantic relevance between queries, we map the user query into the low-dimensional space according to its query vector in the first step. Then, we calculate the uniform resource locator(URL) vector according to the relationship between the query and URL. Finally, we use the improved label propagation algorithm(LPA) and the bipartite graph expansion algorithm to classify the unlabeled Web pages. Experiments show that our methods make about 20% more increase in F1-value than other Web page classification methods based on query log.

信息折叠理念下的Web应用页面视觉流优化设计

针对Web应用页面设计中存在的信息加载过慢、视觉中心散乱等问题,提出基于信息折叠理念的视觉流优化设计方法。采用信息折叠理念设计Web应用页面的信息结构,简洁化传递信息;规划视觉竞争影响因子,并训练随机森林决策树,整合所有决策树并构建视觉流优化模型,调整相应页面的图片和排版顺序,实现Web应用页面视觉流优化设计。实验证明:该方法应用后的页面更加简洁明了,信息传递更加直观,同时页面更加精美,视觉效果突出。

基于Python的Web页面自动登录工具设计与实现

在Web应用中,“登录”功能被广泛应用于用户身份的验证。从Web页面自动登录工具的保持会话需求、参数需求、登录需求和重定向需求出发,抓取并分析请求与响应数据包,确定Web应用的会话参数,完成了基于Python的Web页面自动登录工具设计与实现,并以DVWA漏洞集成网站为靶场对工具进行有效性测试,验证了页面自动登录工具的可用性。

基于Web网页的DOM树链接预分类、信息抽取方法研究

为满足Web网页的数据记录、自动抽取需求,提出依托DOM树特征的网页信息链接预分类、领域本体的Web信息抽取方法,根据HTML、XML网页文档结构设置DOM对象节点树,标记网页页码导航节点、前导符节点、数据特征节点,用DOM根节点到目标节点的标签序列计算树路径,根据各网页链接的XPATH树路径进行分组,DOM树特征预分类模块完成站点信息链接预分类,采用SVM分类器、支持向量机SVM算法,将网页的数据记录样本作出数据信息抽取、属性特征提取计算,从而抽取出满足用户需求的数据文本、数据记录信息。