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Articles de revues sur le sujet « Page Ranking Algorithm »

Auteur : Grafiati

Publié le 7 juin 2025

Mis à jour le 2 août 2025

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Priya, V. Banu, T. Meyyapan ., SM Thamarai, and . "Page Ranking Algorithm for Ranking Web Pages." International Journal of Computer Sciences and Engineering 6, no. 7 (2018): 1502–5. http://dx.doi.org/10.26438/ijcse/v6i7.15021505.

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Li, Xin Li. "Web Page Ranking Algorithm Based on the Meta-Information." Applied Mechanics and Materials 596 (July 2014): 292–96. http://dx.doi.org/10.4028/www.scientific.net/amm.596.292.

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PageRank algorithms only consider hyperlink information, without other page information such as page hits frequency, page update time and web page category. Therefore, the algorithms rank a lot of advertising pages and old pages pretty high and can’t meet the users' needs. This paper further studies the page meta-information such as category, page hits frequency and page update time. The Web page with high hits frequency and with smaller age should get a high rank, while the above two factors are more or less dependent on page category. Experimental results show that the algorithm has good res

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Isha, Mahajan. "Extended Weighted Page Rank Based on VOL by Finding User Activities Time and Page Reading Time, Storing them Directly on Search Engine Database Server." International Journal of Engineering Works (ISSN:2409-2770) 4, no. 2 (2017): 41–48. https://doi.org/10.5281/zenodo.376487.

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Searching on the web can be considered as a process of user enters the query and search system returns a set of most relevant pages in response to user’s query. But results returned are not mostly relevant to user’s query and ranking of the pages are not efficient according to user requirement. In order to improve the precision of ranking of the web pages, after analyzing the different algorithms like Page Rank, Weighted Page Rank, Page Rank based on VOL, Weighted Page Rank algorithm based on VOL. In this paper, we are proposing enhancement by including “User Activities Time” and “Page Reading

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Gupta, Renu, Ankita Shah, Amit Thakkar, and Kamlesh Makvana. "A Survey on Various Web Page Ranking Algorithms." COMPUSOFT: An International Journal of Advanced Computer Technology 05, no. 01 (2016): 2046–52. https://doi.org/10.5281/zenodo.14789798.

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World is full of information and searching is most common task on web. As the amount of information available on web is increasing, it is difficult to acquire relevant information on web. User enters a query for retrieving required information from www and millions of web pages are fetched. These web pages or search results contain both relevant pages and irrelevant search results in response to query submitted by user. For this issue efficient Page Ranking algorithm is needed. Google uses very basic algorithm called Page Rank algorithm which uses web structure mining and has some limitations.

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Abdulrahman, Ayad. "Web Pages Ranking Algorithms: A Survey." Qubahan Academic Journal 1, no. 3 (2021): 29–34. http://dx.doi.org/10.48161/qaj.v1n3a79.

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Due to the daily expansion of the web, the amount of information has increased significantly. Thus, the need for retrieving relevant information has also increased. In order to explore the internet, users depend on various search engines. Search engines face a significant challenge in returning the most relevant results for a user's query. The search engine's performance is determined by the algorithm used to rank web pages, which prioritizes the pages with the most relevancy to appear at the top of the result page. In this paper, various web page ranking algorithms such as Page Rank, Time Ran

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Choudhary, Laxmi, and Rekha Jain. "A Simulation Based Comparative Analysis for Web Pages and Link Queries Using Web Ranking Algorithms." Current Journal of Applied Science and Technology 42, no. 20 (2023): 42–50. http://dx.doi.org/10.9734/cjast/2023/v42i204152.

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In the realm of web information retrieval, the effectiveness of ranking algorithms plays a pivotal role in providing accurate and relevant search results. This simulation-based comparative analysis aims to explore the performance of two prominent ranking algorithms, namely PageRank and Weighted Page Ranking, in the context of web pages and link queries. By leveraging a comprehensive dataset comprising web pages and links, we conduct a meticulous simulation study to evaluate the effectiveness of these algorithms. Through iterative calculations and convergence analysis, we determine the rankings

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Satish Babu, J., T. Ravi Kumar, and Dr Shahana Bano. "Optimizing webpage relevancy using page ranking and content based ranking." International Journal of Engineering & Technology 7, no. 2.7 (2018): 1025. http://dx.doi.org/10.14419/ijet.v7i2.7.12220.

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Systems for web information mining can be isolated into a few classifications as indicated by a sort of mined data and objectives that specif-ic classifications set: Web structure mining, Web utilization mining, and Web Content Mining. This paper proposes another Web Content Mining system for page significance positioning taking into account the page content investigation. The strategy, we call it Page Content Rank (PCR) in the paper, consolidates various heuristics that appear to be critical for breaking down the substance of Web pages. The page significance is resolved on the base of the sig

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Pallavi, *. Dushyant Singh. "HYBRID ALGORITHM FOR PAGE RANKING IN INFORMATION RETRIEVAL SYSTEMS." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 5, no. 9 (2016): 412–19. https://doi.org/10.5281/zenodo.154224.

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Information Retrieval IR systems store a large volume of unstructured data and provide search results for a user query. The performance of the IR systems depends upon the relevancy of the search results with user query. Page ranking algorithms are used to assign rank to the retrieved results for a user query. Page ranking algorithms are mainly categories in to web structure mining and web content mining. In literature many page ranking algorithms have been proposed to improve the relevancy of search results for a user query. In this paper a new hybrid page ranking algorithm using web structure

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Mirzal, Andri. "Search Engine-inspired Ranking Algorithm for Trading Networks." Indonesian Journal of Electrical Engineering and Computer Science 9, no. 3 (2018): 812. http://dx.doi.org/10.11591/ijeecs.v9.i3.pp812-818.

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<p>Ranking algorithms based on link structure of the network are well-known methods in web search engines to improve the quality of the searches. The most famous ones are PageRank and HITS. PageRank uses probability of random surfers to visit a page as the score of that page, and HITS instead of produces one score, proposes using two scores, authority and hub scores, where the authority scores describe the degree of popularity of pages and hub scores describe the quality of hyperlinks on pages. In this paper, we show the differences between WWW network and trading network, and use these

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Andri, Mirzal. "Search Engine-inspired Ranking Algorithm for Trading Networks." Indonesian Journal of Electrical Engineering and Computer Science 9, no. 3 (2018): 812–18. https://doi.org/10.11591/ijeecs.v9.i3.pp812-818.

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Ranking algorithms based on link structure of the network are well-known methods in web search engines to improve the quality of the searches. The most famous ones are PageRank and HITS. PageRank uses probability of random surfers to visit a page as the score of that page, and HITS instead of produces one score, proposes using two scores, authority and hub scores, where the authority scores describe the degree of popularity of pages and hub scores describe the quality of hyperlinks on pages. In this paper, we show the differences between WWW network and trading network, and use these differenc

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Khalil, Nida, Saniah Rehan, Abeer Javed Syed, Khalid Mahboob, Fayyaz Ali та Fatima Waseem. "Optimizing the Eﬃciency of Web Mining through Comparative Web Ranking Algorithms". VFAST Transactions on Software Engineering 11, № 4 (2023): 105–23. http://dx.doi.org/10.21015/vtse.v11i4.1667.

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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 u

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Bama, Sathya, M. S. Irfan Ahmed, and A. Saravanan. "Improved PageRank Algorithm for Web Structure Mining." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 10, no. 9 (2013): 1969–76. http://dx.doi.org/10.24297/ijct.v10i9.1375.

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The growth of internet is increasing continuously by which the need for improving the quality of services has been increased. Web mining is a research area which applies data mining techniques to address all this need. With billions of pages on the web it is very intricate task for the search engines to provide the relevant information to the users. Web structure mining plays a vital role by ranking the web pages based on user query which is the most essential attempt of the web search engines. PageRank, Weighted PageRank and HITS are the commonly used algorithm in web structure mining for ran

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International, Journal on Natural Language Computing (IJNLC). "Ambiguity Resolution in Information Retrieval." International Journal on Natural Language Computing (IJNLC) 2, no. 1 (2023): 7. https://doi.org/10.5121/ijnlc.2013.2101.

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With the advancement of the web it is very difficult to keep up with the amplifying requirements of learning on web, to satisfy user's expectation. Users demand with the updated and accurate results. To solve the queries Search Engines use different techniques. Google the most famous search engine uses Page Ranking Algorithm. Ranking Algorithms arrange the results according to the user's needs. This paper deals with "Page Rank Algorithm". Our proposed algorithm is an extension of page rank algorithm which refines the results so that user gets what he/she expects. We have used a measure Average

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A. Al-Sultany, Ghaidaa, and Asraa A. Abd Al-Ameer. "Locations Ranking using Page Rank Algorithm." International Journal of Engineering & Technology 7, no. 4.19 (2018): 914. http://dx.doi.org/10.14419/ijet.v7i4.19.28070.

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Nowadays, large amount of user-generated data can obtained from social media (e.g., Instagram and Flicker) .People sharing their travel experiences with geo-tagged photo through these media, and the photo itself has important information like title, tags and location.Thesetypes of data provide a new perspective for us to understand the contexts of users. In this paper we focused on tourism service by collecting and analyzing geo-tagged photo from the social media to identify the most popular tourist places and rank them based on user location. We used PageRank algorithm that rank locations bas

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Zhao, Hong, Chen Sheng Bai, and Song Zhu. "Automatic Keyword Extraction Algorithm and Implementation." Applied Mechanics and Materials 44-47 (December 2010): 4041–49. http://dx.doi.org/10.4028/www.scientific.net/amm.44-47.4041.

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Search engines can bring a lot of benefit to the website. For a site, each page’s search engine ranking is very important. To make web page ranking in search engine ahead, Search engine optimization (SEO) make effect on the ranking. Web page needs to set the keywords as “keywords" to use SEO. The paper focuses on the content of a given word, and extracts the keywords of each page by calculating the word frequency. The algorithm is implemented by C # language. Keywords setting of webpage are of great importance on the information and products

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Agyapong, Kwame, J. B. Hayfron Acquah, and M. Asante. "AN OPTIMIZED PAGE RANK ALGORITHM WITH WEB MINING, WEB CONTENT MINING AND WEB STRUCTURE MINING." International Journal of Engineering Technologies and Management Research 4, no. 8 (2020): 22–27. http://dx.doi.org/10.29121/ijetmr.v4.i8.2017.91.

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With the rapid increase in internet technology, users get easily confused in large hypertext structure. The primary goal of the web site owner is to provide the relevant information to the users to fulfill their needs. In order to achieve this goal, they use the concept of web mining. Web mining is used to categorize users and pages by analyzing the users‟ behaviour, the content of the pages, and the order of the URLs that tend to be accessed in order. Most of the search engines are ranking their search results in response to users' queries to make their search navigation easier. With a web br

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Ashish, Chandra Mohammad Suaib and Dr. Rizwan Beg. "GOOGLE SEARCH ALGORITHM UPDATES AGAINST WEB SPAM." Informatics Engineering, an International Journal (IEIJ) 03, mar (2015): 01–10. https://doi.org/10.5121/ieij.2015.3101.

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With the search engines' increasing importance in people's life, there are more and more attempts to illegitimately influence page ranking by means of web spam. Web spam detection is becoming a major challenge for internet search providers. The Web contains a huge number of profit-seeking ventures that are attracted by the prospect of reaching millions of users at a very low cost. There is an economic incentive for manipulating search engine’s listings by creating otherwise useless pages that score high ranking in the search results. Such manipulation is widespread in the industr

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Kwame, Boakye Agyapong, J.B.Hayfron-Acquah Dr., and M. Asante Dr. "AN OPTIMIZED PAGE RANK ALGORITHM WITH WEB MINING, WEB CONTENT MINING AND WEB STRUCTURE MINING." International Journal of Engineering Technologies and Management Research 4, no. 8 (2017): 22–27. https://doi.org/10.5281/zenodo.914660.

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<strong><em>With the rapid increase in internet technology, users get easily confused in large hypertext structure. The primary goal of the web site owner is to provide the relevant information to the users to fulfill their needs. In order to achieve this goal, they use the concept of web mining. Web mining is used to categorize users and pages by analyzing the users" behaviour, the content of the pages, and the order of the URLs that tend to be accessed in order. Most of the search engines are ranking their search results in response to users' queries to make their search navigation easier. W

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Edrees, Zahir, and Henda Juma. "Comparative Analysis of Page Ranking Algorithms for Efficient Information Retrieval." American Journal of Information Science and Technology 9, no. 1 (2025): 15–23. https://doi.org/10.11648/j.ajist.20250901.12.

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Search engines have become crucial tools today, providing users with access to vast amounts of information. At the core of search engine functionality lies the ranking algorithm, which is responsible for determining the relevance and order of web pages returned in response to user queries. Ranking algorithms play a critical role in ensuring that users receive the most relevant and useful results, particularly in the face of exponentially growing web content. This paper provides an in-depth analysis of PageRank algorithms, focusing on their significance in information retrieval systems. The stu

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Sumathi, G., S. Sendhilkumar, and G. S. Mahalakshmi. "Ranking Pages of Clustered Users using Weighted Page Rank Algorithm with User Access Period." International Journal of Intelligent Information Technologies 11, no. 4 (2015): 16–36. http://dx.doi.org/10.4018/ijiit.2015100102.

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The World Wide Web comprises billions of web pages and a tremendous amount of information accessible inside of web pages. To recover obliged data from the World Wide Web, search engines perform number of tasks in light of their separate structural planning. The point at which a user gives a query to the search engine, it commonly returns a bulky number of pages related to the user's query. To backing the users to explore in the returned list, different ranking techniques are connected on the search results. The vast majority of the ranking calculations, which are given in the related work, are

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Yan, Jing Feng, and Shao Hua Tao. "Research of a Novel P2P Search Algorithm Based on Small-World Phenomena." Advanced Materials Research 268-270 (July 2011): 1144–47. http://dx.doi.org/10.4028/www.scientific.net/amr.268-270.1144.

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This paper proposes a novel breadth-first search algorithm and deals with the problem of duplicate web pages removing and page ranking by the principle of Small World phenomena. The features of algorithm in this paper are as follows: 1) it proposes the Unit Tree-based Breadth-First Search Algorithm and performs a qualitative analysis and simulated calculation of the performance, with the results demonstrating that the algorithm in this paper reduces the number of neighboring nodes to which messages shall be forwarded and redundant messages as compared with traditional Breath-First Search algor

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Chawla, Suruchi. "Web Page Recommender System using hybrid of Genetic Algorithm and Trust for Personalized Web Search." Journal of Information Technology Research 11, no. 2 (2018): 110–27. http://dx.doi.org/10.4018/jitr.2018040107.

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The main challenge to effective information retrieval is to optimize the page ranking in order to retrieve relevant documents for user queries. In this article, a method is proposed which uses hybrid of genetic algorithms (GA) and trust for generating the optimal ranking of trusted clicked URLs for web page recommendations. The trusted web pages are selected based on clustered query sessions for GA based optimal ranking in order to retrieve more relevant documents up in ranking and improves the precision of search results. Thus, the optimal ranking of trusted clicked URLs recommends relevant d

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Yasin, Syed Ahmed, and P. V. R. D. Prasada Rao. "Enhanced CRNN-Based Optimal Web Page Classification and Improved Tunicate Swarm Algorithm-Based Re-Ranking." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 30, no. 05 (2022): 813–46. http://dx.doi.org/10.1142/s0218488522500246.

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The main intention of this paper is to develop a new intelligent framework for web page classification and re-ranking. The two main phases of the proposed model are (a) classification, and (b) re-ranking-based retrieval. In the classification phase, pre-processing is initially performed, which follows the steps like HTML (Hyper Text Markup Language) tag removal, punctuation marks removal, stop words removal, and stemming. After pre-processing, word to vector formation is done and then, feature extraction is performed by Principle Component Analysis (PCA). From this, optimal feature selection i

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Bhawsar, Megha, and Shraddha Kumar. "Improved Weight based Web Page Ranking Algorithm." International Journal of Computer Applications 182, no. 29 (2018): 1–5. http://dx.doi.org/10.5120/ijca2018918080.

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ShamiulAmin, M., Shaily Kabir, and Rasel Kabir. "A Score based Web Page Ranking Algorithm." International Journal of Computer Applications 110, no. 12 (2015): 11–15. http://dx.doi.org/10.5120/19367-1035.

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Irfan, Shadab, and Rajesh Kumar Dhanaraj. "BeeRank." International Journal of Swarm Intelligence Research 12, no. 2 (2021): 39–56. http://dx.doi.org/10.4018/ijsir.2021040103.

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There is an incredible change in the world wide web, and the users face difficulty in accessing the needed information as per their need. Different algorithms are devised at each step of the information retrieval process, and it is observed that ranking is one of the core ingredients of any search engine that plays a major role in arranging the information. In this regard, different measures are adopted for ranking the web pages by using content, structure, or log data. The BeeRank algorithm is proposed that provides quality results, which is inspired by the artificial bee colony algorithm for

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Mylsami, T., and B. L. Shivakumar. "Improved Weighted Page Ranking Algorithm Based on Principal Component Analysis and Map Reduce Frame work for Web Access." Asian Journal of Computer Science and Technology 8, no. 2 (2019): 32–39. http://dx.doi.org/10.51983/ajcst-2019.8.2.2144.

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In general the World Wide Web become the most useful information resource used for information retrievals and knowledge discoveries. But the Information on Web to be expand in size and density. The retrieval of the required information on the web is efficiently and effectively to be challenge one. For the tremendous growth of the web has created challenges for the search engine technology. Web mining is an area in which applies data mining techniques to deal the requirements. The following are the popular Web Mining algorithms, such as PageRanking (PR), Weighted PageRanking (WPR) and Hyperlink

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Yang, Fan, and Jun Zhang. "The Ranking Prediction of NBA Playoffs Based on Improved PageRank Algorithm." Complexity 2021 (February 12, 2021): 1–10. http://dx.doi.org/10.1155/2021/6641242.

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It is of great significance to predict the results accurately based on the statistics of sports competition for participants research, commercial cooperation, advertising, and gambling profit. Aiming at the phenomenon that the PageRank page sorting algorithm is prone to subject deviation, the category similarity between pages is introduced into the PageRank algorithm. In the PR value calculation formula of the PageRank algorithm, the factor W(u, v) between pages is added to replace the original Nu (the number of links to page u). In this way, the content category between pages is considered, a

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Musa, Hayat H., and Noureldien A. Noureldien. "Comparing the Ranking Performance of Page Rank Algorithm and Weighted Page Rank Algorithm." Advanced Science Letters 24, no. 1 (2018): 750–53. http://dx.doi.org/10.1166/asl.2018.11807.

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Zeraatkar, Ateye. "Improvement of Page Ranking Algorithm by Negative Score of Spam Pages." Webology 16, no. 2 (2019): 43–56. http://dx.doi.org/10.14704/web/v16i2/a187.

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Gupta, Ashlesha, Ashutosh Dixit, and A. K. Sharma. "An Efficient User Preference and Page Relevance based Page Ranking Algorithm." Indian Journal of Science and Technology 10, no. 37 (2017): 1–7. http://dx.doi.org/10.17485/ijst/2017/v10i37/118688.

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ZHANG, Fang, and Chang-ying GUO. "New page ranking algorithm based on website force." Journal of Computer Applications 32, no. 6 (2013): 1666–69. http://dx.doi.org/10.3724/sp.j.1087.2012.01666.

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Yu, Yang Xin. "Research of Information Retrieval Based on Web Page Segmentation." Applied Mechanics and Materials 204-208 (October 2012): 4928–31. http://dx.doi.org/10.4028/www.scientific.net/amm.204-208.4928.

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A Web information retrieval algorithm based on Web page segment is designed, the key idea of which is to segment each Web page into different topic areas or segments according to its HTML tags and contents since Web pages are semi-structure. First, the algorithm builds a HTML tag tree, and then it combines nodes in the tree under the rule of content similarity and visual similarity. During the process of retrieval and ranking, the algorithm makes full use of the segmentation information to sequence the relevant pages. The experimental results show that this method is able to improve the precis

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Singh, Charanjit, Vijay Laxmi, and Arvinder Singh. "A New Ranking Algorithm for Search Engine: Content’s Weight based Page Ranking." International Journal of Computer Applications 152, no. 7 (2016): 26–28. http://dx.doi.org/10.5120/ijca2016911895.

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Zhang, He Ping, Ya Ping Zhao, and Ya Li Zhao. "Research on PageRank Algorithm to Index Pages." Applied Mechanics and Materials 198-199 (September 2012): 1469–74. http://dx.doi.org/10.4028/www.scientific.net/amm.198-199.1469.

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PageRank algorithm is a vital method to determine the importance of pages. Useful as it is, the algorithm has many disadvantages. Therefore, we arrive at the conclusion that it’s not rational to calculate the importance degree of pages simply by links between them. Considering the timeliness problem of PageRank algorithm, we provide the time penalty factor W(n) to weigh the effects of update time on page ranking. After adding the time penalty factor to the original PageRank algorithm, we come up with the refined PageRank algorithm. Our algorithm is superior compared with the original one and m

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V. Anbazhagu, U., R. Balakrishna, A. Sajeev Ram, and M. Latha. "Web image re-ranking using query specific in cloud computing." International Journal of Engineering & Technology 7, no. 2.21 (2018): 423. http://dx.doi.org/10.14419/ijet.v7i2.21.12458.

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Question answering (QA) allows all users to get information in enhanced technique. In this project we suggest a system for inspiring textual answer with appropriate media data. Our system consists of three components Interpretation median picking, Inquiry propagation, Data pick and Launching. Interpretation median picking is used to select various types of answers. Inquiry propagation is used for extracting the root words from the given query. Data pick and Launching is used for selecting the appropriate answer and producing the result. We use Stemming algorithm, Naïve Bayes classifier algorit

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LIU, Kai-Peng, and Bin-Xing FANG. "A Novel Page Ranking Algorithm Based on Social Annotations." Chinese Journal of Computers 33, no. 6 (2010): 1014–23. http://dx.doi.org/10.3724/sp.j.1016.2010.01014.

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Wu Hengliang, and Zhang Weiwei. "An Improved Page Ranking Algorithm for Web Search Engine." International Journal of Digital Content Technology and its Applications 6, no. 13 (2012): 38–44. http://dx.doi.org/10.4156/jdcta.vol6.issue13.5.

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Usha, M. "A Hybrid Page Ranking Algorithm for Organic Search Results." International Journal for Research in Applied Science and Engineering Technology V, no. VIII (2017): 2348–59. http://dx.doi.org/10.22214/ijraset.2017.8335.

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Jayanthi, J., and Dr K. S. Jayakumar. "An Integrated Page Ranking Algorithm for Personalized Web Search." International Journal of Computer Applications 12, no. 11 (2011): 1–5. http://dx.doi.org/10.5120/1732-2350.

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Suri, Sandeep, Arushi Gupta, and Kapil Sharma. "Comparative Analysis of Ranking Algorithms Used On Web." Annals of Emerging Technologies in Computing 4, no. 2 (2020): 14–25. http://dx.doi.org/10.33166/aetic.2020.02.002.

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With the evolution in technology huge amount of data is being generated, and extracts the necessary data from large volumes of data. This process is significantly complex. Generally the web contains bulk of raw data and the process of converting this data to information mining process can be performed. At whatever point the user places some inquiry on particular web search tool, outcomes are produced with respect to the requests which are dependent on the magnitude of the document created via web information retrieval tools. The results are obtained using calculations and implementation of wel

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Saber, Ali Ali, Aso Kamaran Omer, and Noor Kaylan Hamid. "Google pagerank algorithm: using efficient damping factor." Indonesian Journal of Electrical Engineering and Computer Science 28, no. 3 (2022): 1633. http://dx.doi.org/10.11591/ijeecs.v28.i3.pp1633-1639.

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A vital feature of modern web search engine is the ability to display relevant and reputable pages near the top of the list of query results. A well-used search engine nowadays is Google search engine, it is the world's most popular search engine, rely on PageRank technology to determine a website's ranking. We put our attention on important benefactions to improving the quality of rankings via the value which is called damping factor, commonly the original suggestion d=0.85 by Brin and Page is the most common choice. In this paper, we suggest a new value which plays an important role to rank

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Saber, Ali Ali, Aso Kamaran Omer, and Noor Kaylan Hamid. "Google pagerank algorithm: using efficient damping factor." Indonesian Journal of Electrical Engineering and Computer Science 28, no. 3 (2022): 1633–39. https://doi.org/10.11591/ijeecs.v28.i3.pp1633-1639.

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Résumé :

A vital feature of modern web search engine is the ability to display relevant and reputable pages near the top of the list of query results. A well-used search engine nowadays is Google search engine, it is the world's most popular search engine, rely on PageRank technology to determine a website's ranking. We put our attention on important benefactions to improving the quality of rankings via the value which is called damping factor, commonly the original suggestion d=0.85 by Brin and Page is the most common choice. In this paper, we suggest a new value which plays an important role

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Mohammed. "A Novel Page Ranking Algorithm for a Personalized Web Search." Journal of Computer Science 8, no. 7 (2012): 1029–35. http://dx.doi.org/10.3844/jcssp.2012.1029.1035.

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Henilkumar, Suthar, Rajendra J., and Nikhil Kumar. "Survey Paper on Random surfer model in Page Ranking Algorithm." International Journal of Computer Applications 142, no. 8 (2016): 14–18. http://dx.doi.org/10.5120/ijca2016909880.

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Sharma, Prahlad Kumar, and Sanjay Tiwari. "An Enhanced Page Ranking Algorithm Based on Weights and Third level Ranking of the Webpages." International Journal of Computer Trends and Technology 34, no. 1 (2016): 9–14. http://dx.doi.org/10.14445/22312803/ijctt-v34p102.

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Li, Gai, Liyang Wang, and Weihua Ou. "Robust Personalized Ranking from Implicit Feedback." International Journal of Pattern Recognition and Artificial Intelligence 30, no. 01 (2015): 1659001. http://dx.doi.org/10.1142/s0218001416590011.

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In this paper, we investigate the problem of personalized ranking from implicit feedback (PRIF). It is a more common scenario (e.g. purchase history, click log and page visitation) in recommender systems. The training data are only binary in these problems, reflecting the users’ actions or inactions. One shortcoming of previous PRIF algorithms is noise sensitivity: outliers in training data might bring significant fluctuations in the training process and lead to inaccuracy of the algorithm. In this paper, we propose two robust PRIF algorithms to solve the noise sensitivity problem of existing

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Bharathi, Anand, and S. Rajamohan. "UNVEILING THE DYNAMICS OF SEARCH ENGINE OPTIMIZATION (SEO): STRATEGIES, CHALLENGES AND FUTURE TRENDS IN DIGITAL MARKETING." IITM Journal of Business Studies 12, no. 1 (2025): 52–73. https://doi.org/10.48165/iitmjbs.2025.12.1.3.

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This paper addresses the concept of Search Engine Optimization (SEO), which is essential in increasing the exposure of a website and consequently making it more visible to users. SEO efforts are grouped as on-page, off-page, and technical SEO. On-page SEO includes optimizing the content, adding title, meta, and image tags, and adjusting the URLs of target websites. Promoting domain authority through backlinking, competitor analysis, and link-building strategies is called off-page SEO. Technical SEO is all about enhancing mobile accessibility, XML sitemap, robots. txt, and the security of a web

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Kumar, Munish. "A New Approach for Web Page Ranking Solution: sNorm (p) Algorithm." International Journal of Computer Applications 9, no. 10 (2010): 20–23. http://dx.doi.org/10.5120/1420-1917.

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Patel, Punit. "Research of Page ranking algorithm on Search engine using Damping factor." International Journal of Advance Engineering and Research Development 1, no. 1 (2014): 8–13. http://dx.doi.org/10.21090/ijaerd.0101002.

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