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Literatura académica sobre el tema "Page Ranking Algorithm"

Autor: Grafiati

Publicado: 7 de junio de 2025

Última modificación: 2 de agosto de 2025

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Artículos de revistas sobre el tema "Page Ranking Algorithm"

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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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Libros sobre el tema "Page Ranking Algorithm"

1

Gündüz-Ögüdücü, Şule. Web page recommendation models: Theory and algorithms. Morgan & Claypool, 2011.

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Capítulos de libros sobre el tema "Page Ranking Algorithm"

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Zhang, Xiaocui, and Huilin Wu. "PageRank Algorithm and HITS Algorithm in Web Page Ranking." In Application of Intelligent Systems in Multi-modal Information Analytics. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-74811-1_56.

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Deisy, C., A. M. Rajeswari, R. M. Indra, N. Jayalakshmi, and P. K. Mehalaa Devi. "A Novel Relation-Based Probability Algorithm for Page Ranking in Semantic Web Search Engine." In Information Intelligence, Systems, Technology and Management. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-19423-8_15.

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Sarder, Pinaki, Weixiong Zhang, J. Perren Cobb, and Arye Nehorai. "Gene Reachability Using Page Ranking on Gene Co-expression Networks." In Link Mining: Models, Algorithms, and Applications. Springer New York, 2010. http://dx.doi.org/10.1007/978-1-4419-6515-8_21.

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4

Wang, Ziyang. "Improved Link-Based Algorithms for Ranking Web Pages." In Advances in Web-Age Information Management. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-27772-9_30.

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Agrawal, Nishchay, and Suman Pant. "Web Crawler for Ranking of Websites Based on Web Traffic and Page Views." In Algorithms for Intelligent Systems. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-4087-9_10.

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Mukhopadhyay, Debajyoti, and Pradipta Biswas. "FlexiRank: An Algorithm Offering Flexibility and Accuracy for Ranking the Web Pages." In Distributed Computing and Internet Technology. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11604655_35.

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Sangamuang, Sumalee, Pruet Boonma, and Juggapong Natwichai. "iDBP: A Distributed Min-Cut Density-Balanced Algorithm for Incremental Web-Pages Ranking." In Advances on P2P, Parallel, Grid, Cloud and Internet Computing. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-02607-3_1.

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Pranitha, P., A. Manjula, G. Narsimha, and K. Vaishali. "Optimal Page Ranking Technique for Webpage Personalization Using Semantic Classifier." In Handbook of Artificial Intelligence. BENTHAM SCIENCE PUBLISHERS, 2023. http://dx.doi.org/10.2174/9789815124514123010010.

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Personalized webpage ranking is one of the key components in search engines. Moreover, most of the existing search engines focus only on answering user queries, although personalization will be more and more important as the amount of information available on the Web increases. Even though various re-ranking algorithms are developed, providing prompt responses to the user query results in a major challenge in web page personalization. Therefore, an efficient and effective ranking algorithm named the Oppositional Grass Bee optimization algorithm is developed to re-rank the web documents in the

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Chawla, Suruchi. "Web Page Recommender System using hybrid of Genetic Algorithm and Trust for Personalized Web Search." In Research Anthology on Multi-Industry Uses of Genetic Programming and Algorithms. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-8048-6.ch034.

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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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"Ranking Properties of Spatiotemporal RDF Data." In Advances in Systems Analysis, Software Engineering, and High Performance Computing. IGI Global, 2023. http://dx.doi.org/10.4018/978-1-6684-9108-9.ch004.

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Based on the sorting algorithm, the authors discuss the ranking of spatiotemporal RDF data properties and attempt to improve the query efficiency of large RDF datasets. The chapter introduces three sorting algorithms in machine learning: LR (logistic regression) algorithm, GBDT (gradient boosting decision tree) and FM (factorization machines) model algorithm. After the data sorting system is completed, the authors use A/B test method to test the system. It is self-evident that the recommendation algorithm based on FM ranking is more efficient than linear regression ranking. Using the model per

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Actas de conferencias sobre el tema "Page Ranking Algorithm"

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Gupta, Daya, and Devika Singh. "User preference based page ranking algorithm." In 2016 International Conference on Computing, Communication and Automation (ICCCA). IEEE, 2016. http://dx.doi.org/10.1109/ccaa.2016.7813711.

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Rodrigues, Lissa, and Shree Jaswal. "Hybrid model for improvised page ranking algorithm." In 2015 International Conference on Control, Instrumentation, Communication and Computational Technologies (ICCICCT). IEEE, 2015. http://dx.doi.org/10.1109/iccicct.2015.7475324.

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Alhaidari, Fahd, Sarah Alwarthan, and Abrar Alamoudi. "User Preference Based Weighted Page Ranking Algorithm." In 2020 3rd International Conference on Computer Applications & Information Security (ICCAIS). IEEE, 2020. http://dx.doi.org/10.1109/iccais48893.2020.9096823.

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Alghamdi, Huda, and Fahd Alhaidari. "Extended User Preference Based Weighted Page Ranking Algorithm." In 2021 National Computing Colleges Conference (NCCC). IEEE, 2021. http://dx.doi.org/10.1109/nccc49330.2021.9428844.

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Bennett, Matthew, Julie Stone, and Chaoyang Zhang. "A Scalable Parallel HITS Algorithm for Page Ranking." In 2006 International Multi-Symposiums on Computer and Computational Sciences (IMSCCS). IEEE, 2006. http://dx.doi.org/10.1109/imsccs.2006.22.

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Usha, M., and N. Nagadeepa. "Combined two phase page ranking algorithm for sequencing the web pages." In 2018 2nd International Conference on Inventive Systems and Control (ICISC). IEEE, 2018. http://dx.doi.org/10.1109/icisc.2018.8398925.

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Singh, Vedpal, Anshul Chaudhary, and Pankaj Punia. "OSA - PR: Optimized searching algorithm based on page ranking: Proposed algorithm." In 2012 IEEE International Conference on Engineering Education: Innovative Practices and Future Trends (AICERA). IEEE, 2012. http://dx.doi.org/10.1109/aicera.2012.6306692.

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Sankpal, Lata Jaywant, and Suhas H. Patil. "weWeb Page Re-Ranking using Squirrel Search Rank Algorithm." In 2020 3rd International Conference on Intelligent Sustainable Systems (ICISS). IEEE, 2020. http://dx.doi.org/10.1109/iciss49785.2020.9315998.

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Zambuk, Fatima Umar, Abdulsalam Ya u. Gital, Souley Boukary, Fatsuma Jauro, and Haruna Chiroma. "Evaluation of Iterative Pagerank Algorithm for Web Page Ranking." In 2019 4th International Conference on Electrical, Electronics, Communication, Computer Technologies and Optimization Techniques (ICEECCOT). IEEE, 2019. http://dx.doi.org/10.1109/iceeccot46775.2019.9114728.

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Zhao, Jiakun, and Peihuang Wu. "A Hybrid Page Ranking Algorithm Based on User Behavior." In 2022 7th International Conference on Cloud Computing and Big Data Analytics (ICCCBDA). IEEE, 2022. http://dx.doi.org/10.1109/icccbda55098.2022.9778295.

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