A Comparative Analysis of Search Engine Ranking Algorithms

doi:10.30534/ijatcse/2021/1081022021

Cited by 8 publications

(5 citation statements)

References 17 publications

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“…The search results of the world’s most used search engine—Google search—are ranked according to their relevance to the entered query, as well as according to many additional factors that are part of the search algorithm of an IR system, such as the popularity and the up-to-dateness of the information source ( Hingoro and Nawaz, 2021 ). Each search result usually consists of a hyperlink that refers to the actual information resource, a title, and a brief excerpt or preview of the information resource.…”

Section: Componentsmentioning

confidence: 99%

The Search as Learning Spaceship: Toward a Comprehensive Model of Psychological and Technological Facets of Search as Learning

et al. 2022

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Using a Web search engine is one of today’s most frequent activities. Exploratory search activities which are carried out in order to gain knowledge are conceptualized and denoted as Search as Learning (SAL). In this paper, we introduce a novel framework model which incorporates the perspective of both psychology and computer science to describe the search as learning process by reviewing recent literature. The main entities of the model are the learner who is surrounded by a specific learning context, the interface that mediates between the learner and the information environment, the information retrieval (IR) backend which manages the processes between the interface and the set of Web resources, that is, the collective Web knowledge represented in resources of different modalities. At first, we provide an overview of the current state of the art with regard to the five main entities of our model, before we outline areas of future research to improve our understanding of search as learning processes.

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Section: Componentsmentioning

confidence: 99%

The Search as Learning Spaceship: Toward a Comprehensive Model of Psychological and Technological Facets of Search as Learning

et al. 2022

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“…The main quality of a search engine is the efficiency of its ranking system [23]. The main filing systems are as follows:…”

Section: Ranking Of the Resultsmentioning

confidence: 99%

Search Engines in Learning Contexts: A Literature Review

Homte

Batchakui

Nkambou

2022

Int. J. Emerg. Technol. Learn.

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The web is one of the primary sources of information for finding learning oriented documents. In addition, the main suitable way to find information and documents on the Internet is by using search engines. Search engines are constantly improving in terms of selection algorithms and in terms of the Human Machine interface (HMI). Also, these search engines are the basis of a new field of research called Search-As-Learning. The Search-As-Learning explores information search environments to enhance learning during user search tasks. This work focuses on our view of the state of the art in the field of Search Engines in learning context and Search-As-Learning, stressing on the most recent research. We conclude by highlighting the current shortcomings on improvement of the learning aspect within search engines, and present next work which will be the association of a layer above the traditional search engines to promote the appropriation of content during search task for a learning context

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“…The page rank algorithm distributes the value of the page rank to all out-linking web pages equally, although not all out links are equally essential or relevant to the user query. This difficulty can be handled by adding a weight component to the page rank analysis, resulting in a new technique recognized as the Weighted Page Rank algorithm [19].…”

Section: Theoretical Frameworkmentioning

confidence: 99%

Optimizing the Eﬃciency of Web Mining through Comparative Web Ranking Algorithms

Khalil,

Rehan,

Syed

et al. 2023

VFAST trans. softw. eng.

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Millions of web pages carrying massive amounts of data make up the World Wide Web. Real-time data has been generated on a wide scale on the websites. However, not every piece of data is relevant to the user. While scouring the web for information, a user may come upon a web page that contains irrelevant or incomplete information. As a response, search engines can alleviate this issue by displaying the most relevant pages. Two web page ranking algorithms are proposed in this study along with the Dijkstra algorithm; the PageRank algorithm and the Weighted PageRank algorithm. The algorithms are used to evaluate a web page's importance or relevancy within a network, such as the Internet. PageRank evaluates a page's value based on the quantity and quality of links leading to it. It is commonly utilized by nearly all search engines around the world to rank web pages in order of relevance. This algorithm is used by Google, the most widespread Internet search engine. In the process of Web mining, page rank is quite weighty. The most important component of marketing is online use mining, which investigates how people browse and operate a business on a company's website. The study presents two proposed models that try to optimize web links and improve search engine results relevancy for users.

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A Comparative Analysis of Search Engine Ranking Algorithms

Cited by 8 publications

References 17 publications

The Search as Learning Spaceship: Toward a Comprehensive Model of Psychological and Technological Facets of Search as Learning

The Search as Learning Spaceship: Toward a Comprehensive Model of Psychological and Technological Facets of Search as Learning

Search Engines in Learning Contexts: A Literature Review

Optimizing the Eﬃciency of Web Mining through Comparative Web Ranking Algorithms

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