scholarly journals A Comparative Analysis of Search Engine Ranking Algorithms

2021 ◽  
Vol 10 (2) ◽  
pp. 1247-1252
Keyword(s):  
Search Engine ◽  
Search Engines ◽  
Ranking Algorithm ◽  
Video Content ◽  
Uniform Resource Locator ◽  
Page Rank ◽  
Ranking Algorithms ◽  
Content Search ◽  
Google Search ◽  
Better Than

Ranking Algorithm is the most proper way of positioning on a scale. As the information and knowledge on the internet are increasing every day.The search engine's ability to deliver the most appropriate material to the customer. It is more and more challenging without even any assistance in filtering through all of it. However, searching what user requires is extremely difficult. In this research, an effort has been made to compare and analyze the most popular and effective search engines. The keywords were used in uniform resource locator like, title tag, header, or even the keyword's resembles to the actual text. The page rank algorithm computes a perfect judgment of how relevant a webpage is by analyzing the quality and calculating the number of links connected to it. In this study the keyword relevancy and time response were used for search engines and observed the results. It is observed that the google search engine is faster than the bing and youtube, and after all, bing is the best search engine after google. Moreover, youtube is the fastest search engine in terms of video content search. The google results were found more accurate. However, it is better than all of the search engine

scholarly journals Search Engine Optimization with Google-Web

2019 ◽  
pp. 227-230
Author(s):  
Pratik C. Jambhale
Keyword(s):  
Search Engine ◽  
Search Engines ◽  
Page Rank ◽  
Web Presence ◽  
Search Engine Optimization ◽  
Search Results ◽  
Web Document ◽  
Search Patterns ◽  
User Friendly ◽  
Google Search

Search engine optimization is a technique to take a web document in top search results of a search engine. Web presence Companies is not only an easy way to reach among the target users but it may be profitable for Business is exactly find the target users as of the reason that most of the time user search out with the keywords of their use rather than searching the Company name, and if the Company Website page come in the top positions then the page may be profitable. This work describes the tweaks of taking the page on top position in Google by increasing the Page rank which may result in the improved visibility and profitable deal for a Business. Google is most user-friendly search engine to prove for the all users which give user-oriented results. In addition ,most of other search engines use Google search patterns so we have concentrated on it. So, if a page is Register on Google it Is Display on most of the search engines.

scholarly journals Meta Search Engine using Semantic Similarity and Correlation Coefficient

2020 ◽  
Vol 9 (9) ◽  
pp. 643-647
Keyword(s):  
Search Engine ◽  
Search Engines ◽  
Search Space ◽  
The Other ◽  
Ranking Algorithm ◽  
Search Results ◽  
Ranking Algorithms ◽  
Meta Search Engine ◽  
User Query ◽  
Meta Search

This paper aims to provide an intelligent way to query and rank the results of a Meta Search Engine. A Meta Search Engine takes input from the user and produces results which are gathered from other search engines. The main advantage of a Meta Search Engine over methodical search engine is its ability to extend the search space and allows more resources for the user. The semantic intelligent queries will be fetching the results from different search engines and the responses will be fed into our ranking algorithm. Ranking of the search results is the other important aspect of Meta search engines. When a user searches a query, there are number of results retrieved from different search engines, but only several results are relevant to user's interest and others are not much relevant. Hence, it is important to rank results according to the relevancy with user query. The proposed paper uses intelligent query and ranking algorithms in order to provide intelligent meta search engine with semantic understanding.

scholarly journals Search Engine Gender Bias

2021 ◽  
Vol 4 ◽  
Author(s):  
Fons Wijnhoven ◽  
Jeanna van Haren
Keyword(s):  
Political Participation ◽  
Search Engine ◽  
Job Search ◽  
Value Chain ◽  
Page Rank ◽  
Ethical Implications ◽  
Ranking Algorithms ◽  
Job Searches ◽  
Google Search ◽  
Rank Differences

This article discusses possible search engine page rank biases as a consequence of search engine profile information. After describing search engine biases, their causes, and their ethical implications, we present data about the Google search engine (GSE) and DuckDuckGo (DDG) for which only the first uses profile data for the production of page ranks. We analyze 408 search engine screen prints of 102 volunteers (53 male and 49 female) on queries for job search and political participation. For job searches via GSE, we find a bias toward stereotypically “female” jobs for women but also for men, although the bias is significantly stronger for women. For political participation, the bias of GSE is toward more powerful positions. Contrary to our hypothesis, this bias is even stronger for women than for men. Our analysis of DDG does not give statistically significant page rank differences for male and female users. We, therefore, conclude that GSE’s personal profiling is not reinforcing a gender stereotype. Although no gender differences in page ranks was found for DDG, DDG usage in general gave a bias toward “male-dominant” vacancies for both men and women. We, therefore, believe that search engine page ranks are not biased by profile ranking algorithms, but that page rank biases may be caused by many other factors in the search engine’s value chain. We propose ten search engine bias factors with virtue ethical implications for further research.

Critical Analysis of Major Search Engines

2019 ◽  
Vol 16 (9) ◽  
pp. 3712-3716
Author(s):  
Kailash Kumar ◽  
Abdulaziz Al-Besher
Keyword(s):  
Search Engine ◽  
Search Engines ◽  
Critical Analysis ◽  
Research Paper ◽  
Web Pages ◽  
Ranking Algorithm ◽  
Sponsored Search ◽  
Web Page ◽  
Rigorous Analysis ◽  
The Common

This paper examines the overlapping of the results retrieved between three major search engines namely Google, Yahoo and Bing. A rigorous analysis of overlap among these search engines was conducted on 100 random queries. The overlap of first ten web page results, i.e., hundred results from each search engine and only non-sponsored results from these above major search engines were taken into consideration. Search engines have their own frequency of updates and ranking of results based on their relevance. Moreover, sponsored search advertisers are different for different search engines. Single search engine cannot index all Web pages. In this research paper, the overlapping analysis of the results were carried out between October 1, 2018 to October 31, 2018 among these major search engines namely, Google, Yahoo and Bing. A framework is built in Java to analyze the overlap among these search engines. This framework eliminates the common results and merges them in a unified list. It also uses the ranking algorithm to re-rank the search engine results and displays it back to the user.

scholarly journals Ranking by Relevance and Citation Counts, a Comparative Study: Google Scholar, Microsoft Academic, WoS and Scopus

2019 ◽  
Vol 11 (9) ◽  
pp. 202 ◽  
Author(s):  
Rovira ◽  
Codina ◽  
Guerrero-Solé ◽  
Lopezosa
Keyword(s):  
Search Engine ◽  
Search Engines ◽  
Web Of Science ◽  
Growth Potential ◽  
Google Scholar ◽  
Unexpected Finding ◽  
Ranking Algorithms ◽  
Microsoft Academic ◽  
Citation Counts ◽  
The Impact

Search engine optimization (SEO) constitutes the set of methods designed to increase the visibility of, and the number of visits to, a web page by means of its ranking on the search engine results pages. Recently, SEO has also been applied to academic databases and search engines, in a trend that is in constant growth. This new approach, known as academic SEO (ASEO), has generated a field of study with considerable future growth potential due to the impact of open science. The study reported here forms part of this new field of analysis. The ranking of results is a key aspect in any information system since it determines the way in which these results are presented to the user. The aim of this study is to analyze and compare the relevance ranking algorithms employed by various academic platforms to identify the importance of citations received in their algorithms. Specifically, we analyze two search engines and two bibliographic databases: Google Scholar and Microsoft Academic, on the one hand, and Web of Science and Scopus, on the other. A reverse engineering methodology is employed based on the statistical analysis of Spearman’s correlation coefficients. The results indicate that the ranking algorithms used by Google Scholar and Microsoft are the two that are most heavily influenced by citations received. Indeed, citation counts are clearly the main SEO factor in these academic search engines. An unexpected finding is that, at certain points in time, Web of Science (WoS) used citations received as a key ranking factor, despite the fact that WoS support documents claim this factor does not intervene.

scholarly journals Similarity Measurement Technique for Measuring the Performance of Page Rank Algorithm Based on Hadoop

2020 ◽  
Vol 8 (5) ◽  
pp. 4712-4717
Keyword(s):  
Big Data ◽  
Search Engine ◽  
Measurement Technique ◽  
Similarity Measurement ◽  
Web Pages ◽  
Ranking Algorithm ◽  
Web Data ◽  
Mapreduce Framework ◽  
Page Rank ◽  
Hadoop Mapreduce

In this century big data manipulation is a challenging task in the field of web mining because content of web data is massively increasing day by day. Using search engine retrieving efficient, relevant and meaningful information from massive amount of Web Data is quite impossible. Different search engine uses different ranking algorithm to retrieve relevant information easily. A new page ranking algorithm is presented based on synonymous word count using Hadoop MapReduce framework named as Similarity Measurement Technique (SMT). Hadoop MapReduce framework is used to partition Big Data and provides a scalable, economical and easier way to process these data. It stores intermediate result for running iterative jobs in the local disk. In this algorithm, SMT takes a query from user and parse it using Hadoop and calculate rank of web pages. For experimental purpose wiki data file have been used and applied page rank algorithm (PR), improvised page rank algorithm (IPR) and proposed SMT method to calculate page rank of all web pages and compare among these methods. Proposed method provides better scoring accuracy than other approaches and reduces theme drift problem.

scholarly journals Quantification of competitive value of documents

2009 ◽  
Vol 57 (5) ◽  
pp. 285-290
Author(s):  
Pavel Šimek ◽  
Jiří Vaněk ◽  
Jan Jarolímek
Keyword(s):  
Search Engine ◽  
Market Share ◽  
Full Text ◽  
Search Engines ◽  
Web Site ◽  
Optimization Techniques ◽  
Text Search ◽  
Full Text Search ◽  
Google Search ◽  
The Web

The majority of Internet users use the global network to search for different information using fulltext search engines such as Google, Yahoo!, or Seznam. The web presentation operators are trying, with the help of different optimization techniques, to get to the top places in the results of fulltext search engines. Right there is a great importance of Search Engine Optimization and Search Engine Marketing, because normal users usually try links only on the first few pages of the fulltext search engines results on certain keywords and in catalogs they use primarily hierarchically higher placed links in each category. Key to success is the application of optimization methods which deal with the issue of keywords, structure and quality of content, domain names, individual sites and quantity and reliability of backward links. The process is demanding, long-lasting and without a guaranteed outcome. A website operator without advanced analytical tools do not identify the contribution of individual documents from which the entire web site consists. If the web presentation operators want to have an overview of their documents and web site in global, it is appropriate to quantify these positions in a specific way, depending on specific key words. For this purpose serves the quantification of competitive value of documents, which consequently sets global competitive value of a web site. Quantification of competitive values is performed on a specific full-text search engine. For each full-text search engine can be and often are, different results. According to published reports of ClickZ agency or Market Share is according to the number of searches by English-speaking users most widely used Google search engine, which has a market share of more than 80%. The whole procedure of quantification of competitive values is common, however, the initial step which is the analysis of keywords depends on a choice of the fulltext search engine.

scholarly journals Analysis of Web Scraping Techniques to Get Keywords Suggestion and Allintitle Automatically from Google Search Engines

Compiler ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 71
Author(s):  
Aris Wahyu Murdiyanto ◽  
Adri Priadana
Keyword(s):  
Search Engine ◽  
Search Engines ◽  
Evaluation Result ◽  
Search Engine Optimization ◽  
Web Scraping ◽  
Long Time ◽  
Google Search ◽  
The Web

Keyword research is one of the essential activities in Search Engine Optimization (SEO). One of the techniques in doing keyword research is to find out how many articles titles on a website indexed by the Google search engine contain a particular keyword or so-called "allintitle". Moreover, search engines are also able to provide keywords suggestion. Getting keywords suggestions and allintitle will not be effective, efficient, and economical if done manually for relatively extensive keyword research. It will take a long time to decide whether a keyword is needed to be optimized. Based on these problems, this study aimed to analyze the implementation of the web scraping technique to get relevant keyword suggestions from the Google search engine and the number of "allintitle" that are owned automatically. The data used as an experiment in this test consists of ten keywords, which each keyword would generate a maximum of ten keywords suggestion. Therefore, from ten keywords, it will produce at most 100 keywords suggestions and the number of allintitles. Based on the evaluation result, we got an accuracy of 100%. It indicated that the technique could be applied to get keywords suggestions and allintitle from Google search engines with outstanding accuracy values.

scholarly journals "DNS SEHAT" Implementation on Automatic Replicated Recursive Domain Name System on PT. X

2019 ◽  
Author(s):  
Muhammad Ilham Verardi Pradana
Keyword(s):  
Search Engine ◽  
Search Engines ◽  
The Other ◽  
The Internet ◽  
Domain Name System ◽  
Domain Name ◽  
Search Results ◽  
Search Result ◽  
Google Search

Thanks to the existence of Search engines, all of informations and datas could be easily found in the internet, one of the search engine that users use the most is Google. Google still be the most popular search engine to provide any informations available on the internet. The search result that Google provide, doesn't always give the result we wanted. Google just displayed the results based on the keyword we type. So sometimes, they show us the negative contents on the internet, such as pornography, pornsites, and many more that seems to be related to the keyword, whether the title or the other that makes the result going that way. In this paper, we will implement the "DNS SEHAT" to pass along client's request queries so the Google search engine on the client's side will provide more relevant search results without any negative contents.

A cross-sectional study on quality of diabetes information identified from the Internet (Preprint)

2019 ◽  
Author(s):  
Jingchun Fan ◽  
Jean Craig ◽  
Na Zhao ◽  
Fujian Song
Keyword(s):  
Health Information ◽  
Search Engine ◽  
Search Engines ◽  
The Internet ◽  
Online Health Information ◽  
Online Information ◽  
Significant Difference ◽  
Content Coverage ◽  
Google Search

BACKGROUND Increasingly people seek health information from the Internet, in particular, health information on diseases that require intensive self-management, such as diabetes. However, the Internet is largely unregulated and the quality of online health information may not be credible. OBJECTIVE To assess the quality of online information on diabetes identified from the Internet. METHODS We used the single term “diabetes” or equivalent Chinese characters to search Google and Baidu respectively. The first 50 websites retrieved from each of the two search engines were screened for eligibility using pre-determined inclusion and exclusion criteria. Included websites were assessed on four domains: accessibility, content coverage, validity and readability. RESULTS We included 26 websites from Google search engine and 34 from Baidu search engine. There were significant differences in website provider (P<0.0001), but not in targeted population (P=0.832) and publication types (P=0.378), between the two search engines. The website accessibility was not statistically significantly different between the two search engines, although there were significant differences in items regarding website content coverage. There was no statistically significant difference in website validity between the Google and Baidu search engines (mean Discern score 3.3 vs 2.9, p=0.156). The results to appraise readability for English website showed that that Flesch Reading Ease scores ranged from 23.1 to 73.0 and the mean score of Flesch-Kincaid Grade Level ranged range from 5.7 to 19.6. CONCLUSIONS The content coverage of the health information for patients with diabetes in English search engine tended to be more comprehensive than that from Chinese search engine. There was a lack of websites provided by health organisations in China. The quality of online health information for people with diabetes needs to be improved to bridge the knowledge gap between website service and public demand.

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