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Academic literature on the topic 'Webpage ranking'

Author: Grafiati

Published: 4 June 2021

Last updated: 4 February 2022

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Journal articles on the topic "Webpage ranking"

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Sankpal, Lata Jaywant, and Suhas H. Patil. "Rider-Rank Algorithm-Based Feature Extraction for Re-ranking the Webpages in the Search Engine." Computer Journal 63, no. 10 (June 12, 2020): 1479–89. http://dx.doi.org/10.1093/comjnl/bxaa032.

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Abstract The webpage re-ranking is a challenging task while retrieving the webpages based on the query of the user. Even though the webpages in the search engines are ordered depends on the importance of the content, retrieving the necessary documents based on the input query is quite difficult. Hence, it is required to re-rank the webpages available in the websites based on the features of the pages in the search engines, like Google and Bing. Thus, an effective Rider-Rank algorithm is proposed to re-rank the webpages based on the Rider Optimization Algorithm (ROA). The input queries are forwarded to different search engines, and the webpages generated from the search engines with respect to the input query are gathered. Initially, the keywords are generated for the webpages. Then, the top keyword is selected, and the features are extracted from the top keyword using factor-based, text-based and rank-based features of the webpage. Finally, the webpages are re-ranked using the Rider-Rank algorithm. The performance of the proposed approach is analyzed based on the metrics, such as F-measure, recall and precision. From the analysis, it can be shown that the proposed algorithm obtains the F-measure, recall and precision of 0.90, 0.98 and 0.84, respectively.

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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 (March 18, 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 significance of terms which the page contains. The significance of a term is determined concern-ing a given inquiry q and it depends on its measurable and linguistic elements. As a source set of pages for mining we utilize an arrangement of pages reacted by a web search tool to the question q. PCR utilizes a neural system as its inward order structure. We depict a usage of the proposed strategy and an examination of its outcomes with the other existing characterization framework –page rank algorithm.

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Zhang, Shao Xuan, and Tian Liu. "A Webpage Ranking Algorithm Based on Collaborative Recommendation." Advanced Materials Research 765-767 (September 2013): 998–1002. http://dx.doi.org/10.4028/www.scientific.net/amr.765-767.998.

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In view of the present personalized ranking of search results user interest model construction difficult, relevant calculation imprecise problems, proposes a combination of user interest model and collaborative recommendation algorithm for personalized ranking method. The method from the user search history, including the submit query, click the relevant webpage information to train users interest model, then using collaborative recommendation algorithm to obtain with common interests and neighbor users, on the basis of these neighbors on the webpage and webpage recommendation level associated with the users to sort the search results. Experimental results show that: the algorithm the average minimum precision than general sorting algorithm was increased by about 0.1, with an increase in the number of neighbors of the user, minimum accuracy increased. Compared with other ranking algorithms, using collaborative recommendation algorithm is helpful for improving webpage with the user interest relevance precision, thereby improving the sorting efficiency, help to improve the search experience of the user.

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Hong, Ying, and Zeng Min Geng. "Research and Realization of a Search Engine System for Professional Field." Advanced Materials Research 850-851 (December 2013): 745–50. http://dx.doi.org/10.4028/www.scientific.net/amr.850-851.745.

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In the light of the deficiency of general search engine technology in professional retrieval,This paper researched and designed a search engine system for professional field (SESPF for short).This system automatically crawls web pages by the spider program.It introduced professional dictionary and filtered the webpages information according to certain rules.At the same time,the system improved the PageRank algorithm and Lucene webpage ranking algorithm.The experimental results show that this system has a higher precision in professional field retrieval compared with the general search engine.

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K.G., Srinivasa, Anil Kumar Muppalla, Bharghava Varun A., and Amulya M. "MapReduce Based Information Retrieval Algorithms for Efficient Ranking of Webpages." International Journal of Information Retrieval Research 1, no. 4 (October 2011): 23–37. http://dx.doi.org/10.4018/ijirr.2011100102.

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In this paper, the authors discuss the MapReduce implementation of crawler, indexer and ranking algorithms in search engines. The proposed algorithms are used in search engines to retrieve results from the World Wide Web. A crawler and an indexer in a MapReduce environment are used to improve the speed of crawling and indexing. The proposed ranking algorithm is an iterative method that makes use of the link structure of the Web and is developed using MapReduce framework to improve the speed of convergence of ranking the WebPages. Categorization is used to retrieve and order the results according to the user choice to personalize the search. A new score is introduced in this paper that is associated with each WebPage and is calculated using user’s query and number of occurrences of the terms in the query in the document corpus. The experiments are conducted on Web graph datasets and the results are compared with the serial versions of crawler, indexer and ranking algorithms.

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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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Rahman, Md Mahbubur, Samsuddin Ahmed, Md Syful Islam, and Md Moshiur Rahman. "An Effective Ranking Method of Webpage Through TFIDF and Hyperlink Classified Pagerank." International Journal of Data Mining & Knowledge Management Process 3, no. 4 (July 31, 2013): 149–56. http://dx.doi.org/10.5121/ijdkp.2013.3411.

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Sangamuang, Sumalee, Pruet Boonma, Juggapong Natwichai, and Wanpracha Art Chaovalitwongse. "Impact of minimum-cut density-balanced partitioning solutions in distributed webpage ranking." Optimization Letters 14, no. 3 (February 13, 2019): 521–33. http://dx.doi.org/10.1007/s11590-019-01399-9.

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Makkar, Aaisha, and Neeraj Kumar. "User behavior analysis-based smart energy management for webpage ranking: Learning automata-based solution." Sustainable Computing: Informatics and Systems 20 (December 2018): 174–91. http://dx.doi.org/10.1016/j.suscom.2018.02.003.

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Poulos, Marios, Sozon Papavlasopoulos, V. S. Belesiotis, and Nikolaos Korfiatis. "A semantic self-organising webpage-ranking algorithm using computational geometry across different knowledge domains." International Journal of Knowledge and Web Intelligence 1, no. 1/2 (2009): 24. http://dx.doi.org/10.1504/ijkwi.2009.027924.

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Dissertations / Theses on the topic "Webpage ranking"

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Kritzinger, Wouter Thomas. "The effect webpage body keywords location has on ranking in search engines results : an empirical study /." Thesis, Click here for online access, 2005. http://dk.cput.ac.za/cgi/viewcontent.cgi?article=1077&context=td_cput.

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Chang, Shen-Yi, and 張聖益. "A Keyword Recommending Mechanism for Improving Webpage Ranking." Thesis, 2006. http://ndltd.ncl.edu.tw/handle/02721938545188635923.

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碩士
元智大學
資訊管理學系
94
Search Engine is one of the popular tools people used to look for information on the World Wide Web. Recent studies have shown that a majority of web page accesses are referred by search engines. To find a page on the Web, many Web users go to their favorite search engine, issue keyword queries, and look at the results. When search engines constantly return popular pages at the top of their search results, more Web users will discover and look at those pages, increasing their popularity even further. Thus, to become the top of the search results can help to increase the page popularity. Studies have shown that page contents is one of an important factor which search engines rank web pages, thus, to select the valuable keywords could not only improve the page contents but also the page ranking. But none of the related works show how to find the valuable keywords. In order to find the valuable keywords systematically, this thesis, we establish a keyword recommendation mechanism by using Local Feedback method. Our study shows more keywords can be discovered and rank of page results can be consequently improved.

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Κόλλιας, Γεώργιος. "Αρχιτεκτονικές λογισμικού για περιβάλλοντα επίλυσης προβλημάτων και εφαρμογές στο ασύγχρονο μοντέλο υπολογισμού." Thesis, 2009. http://nemertes.lis.upatras.gr/jspui/handle/10889/2525.

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Τα τελευταία χρόνια έχουν γίνει σημαντικές προσπάθειες o Πληροφορικός-Επιστήμονας των Υπολογισμών να εκθέσει με εύληπτο τρόπο τη γνώση και εμπειρία του στις κοινότητες εκείνων που θέλουν να κάνουν υπολογισμούς. Κάτι τέτοιο έχει καταστεί δυνατό με την κατασκευή σύνθετων στη δομή, αλλά εύκολων στη χρήση, εργαλείων-περιβαλλόντων υπολογισμού στα οποία κανείς μπορεί με εντελώς φυσικό τρόπο να προδιαγράψει το πρόβλημά του και -ανάλογα με την εμπειρία του- να επέμβει στη ροή επίλυσής του. Τα Περιβάλλοντα Επίλυσης Προβλημάτων (ΠΕΠ) προβάλλουν λοιπόν ως μια πολύ ελκυστική λύση για τον επιστήμονα των εφαρμογών που αναζητεί μια εύχρηστη, ισχυρή και αξιόπιστη πλατφόρμα λογισμικού για τους υπολογισμούς του. Σε πολλές περιπτώσεις αυτοί οι υπολογισμοί είναι πολύ μεγάλης κλίμακας και απαιτούν πολυάριθμους και αποδοτικούς πόρους. Η τιθάσευσή τους σε κάποια έκταση έγινε δυνατή με τη στροφή σε παράλληλες-κατανεμημένες αρχιτεκτονικές, πρόσφατα μεγάλης κλίμακας, με έμφαση στην ευχρηστία, στην ασφάλεια πρόσβασης και στη συνεργατικότητα (Πλέγμα (Grid)). Σε άλλες περιπτώσεις οι πολυπύρηνοι επεξεργαστές που εξοπλίζουν πλέον τους τυπικούς οικιακούς υπολογιστές μας και οι προβλέψεις για αθρόα κλιμάκωση του αριθμού των προσφερόμενων πυρήνων, προτρέπουν σε επαναδιαπραγμάτευση κλασικών αλγορίθμων με στόχευση στην εξαγωγή παραλληλίας, αφού πλέον αυτή μπορεί να απεικονιστεί άμεσα στο διαθέσιμο υλικό. Επιπρόσθετα μια τέτοια στροφή ώθησε και τη διερεύνηση εναλλακτικών μοντέλων υπολογισμού: Το ασύγχρονο μοντέλο υπολογισμού προσφέροντας τη δυνατότητα για εξάλειψη των χρονοβόρων φάσεων συγχρονισμού των πολλαπλών μονάδων επεξεργασίας προβάλλει ως μια ενδιαφέρουσα επιλογή. Συστηματοποιούμε τη μελέτη των Περιβαλλόντων Επίλυσης Προβλημάτων (ΠΕΠ) εντοπίζοντας τους άξονες που χαρακτηρίζουν αυτήν την κατηγορία συστημάτων λογισμικού και υλοποιώντας το Jylab, ένα πρωτότυπο ΠΕΠ με έμφαση στη φορητότητα, την επαναχρησιμοποίηση ελεύθερα διαθέσιμου κώδικα και τη δυνατότητα για ακολουθιακό, παράλληλο και κατανεμημένο υπολογισμό σε πολλαπλές πλατφόρμες. Ειδικότερα, το Jylab περιλαμβάνει υποστήριξη για ασύγχρονο κατανεμημένο υπολογισμό, ανάλυση ιστογραφημάτων και εκτέλεση υπολογισμών στο Πλέγμα (Grid). Αμέσως μετά εισάγουμε το ασύγχρονο μοντέλο υπολογισμού εστιάζοντας σε καίρια ζητήματα όπως η ανάλυση της σύγκλισης, η ανίχνευση του τερματισμού και η υλοποίησή του. Προτείνουμε πιθανοτικό πλαίσιο εντοπισμού της σύγκλισης και διερευνούμε την πολυπλοκότητα του μοντέλου. Στη συνέχεια μελετούμε αλγορίθμους διάταξης των κόμβων ενός γραφήματος, επικεντρώνοντας στον υπολογισμό του διανύσματος του PageRank το οποίο χρησιμοποιεί η Google για να διατάξει τα αποτελέσματα μιας ερώτησης που υποβάλλουμε στη μηχανή αναζήτησής της. Αποδεικνύουμε πως και άλλες μέθοδοι διάταξης, οι οποίες εκφράζονται πρωταρχικά ως δυναμοσειρές ενός τροποποιημένου μητρώου συνδέσμων μπορούν να γραφτούν ως γινόμενα των επαναληπτικών μητρώων που χρησιμοποιούνται στον υπολογισμό του διανύσματος PageRank, αλλά με διαφορετική παράμετρο σε κάθε όρο τους (μέθοδος της πολυπαραμετρικής απόσβεσης). Στη συνέχεια εκθέτουμε την πειραματική συμπεριφορά του ασύγχρονου μοντέλου, όπως αυτή προκύπτει από υλοποιήσεις κυρίως του αλγορίθμου του PageRank, σε διάφορες πλατφόρμες (τοπικά, στη συστάδα υπολογισμών και στο Πλέγμα (Grid)) και με μονάδες εκτέλεσης νήματα ή διεργασίες. To Jylab χρησιμοποιήθηκε εντατικά σε αυτές τις διερευνήσεις και αποδείχτηκε πως όλοι οι πειραματισμοί μπορούν να τεθούν κάτω από ενιαίο πλαίσιο λογισμικού. Επίσης εισάγουμε μια κλάση αλγορίθμων κατανεμημένου υπολογισμού στατιστικών μεγεθών, τους gossip αλγορίθμους, σε κάθε στοιχειώδες βήμα των οποίων μόνο δύο οντότητες επικοινωνούν και υπολογίζουν. Επεκτείνουμε αυτούς τους αλγορίθμους επιτρέποντας σε k > 2 οντότητες να αλληλεπιδρούν ανά βήμα, προσομοιώνουμε τη συμπεριφορά τους και προτείνουμε πρωτόκολλα υλοποίησής τους.
In recent years computational scientists strive to expose their knowledge and experience to the communities of people interested in performing computations. This endeavor focuses on the construction of complex in structure, however simple in use, toolchains and environments in which a researcher can specify his or her problem and - depending on his experience - change its exact solution flow. In many cases these computations necessitate large-scale and performant resources. Harnessing them, to some extent, became possible by turning to parallel-distributed architectures, recently of large scale, emphasizing usability, security in accessing them and collaboration perspectives (Grid). In other cases, the multicore processors, nowadays powering even typical personal computers, coupled with predictions for dramatic increase in the number of available cores in the near future, suggest a reconsideration of classic algorithms aiming at extracting parallelism, since this can be directly mapped to underlying hardware. Additionally, such a move, also fuels the investigation of alternative computation models: The asynchronous computation model, offering the flexibility for the complete removal of time-consuming synchronization phases, is a very interesting option. We study Problem Solving Environments (PSEs) in a systematic manner, specifying the axes characterizing this category of systems of software also implementing Jylab, a prototype PSE emphasizing portability and the reuse of freely available code and enabling sequential, parallel and distributed computing over multiple platforms. More specifically, Jylab includes support for asynchronous distributed computations, Web graph analysis and Grid computing. Then we introduce the asynchronous computation model, focusing in three core subjects, namely its convergence analysis, the termination detection problem and its implementation. We propose a probabilistic framework for convergence detection and explore the complexity of the model. Afterwards, we survey algorithms for ranking the nodes of a graph, focusing on computing the PageRank vector, which is used by Google for ranking the results of a query submitted to its search engine. We prove that a whole class of ranking methods, primarily expressed as a power series of a modified link matrix can be written as products of iterative matrices similar to those used in computing the PageRank vector, albeit with a different damping parameter for each of its terms (multidamping). Next, we present the experimental behavior of the asynchronous model, mainly as applied in computing the PageRank vector, over different platforms (locally, in a computer cluster and over the Grid) using either threads or processes as its units of execution. Jylab was intensively used in these investigations and it was proved that all experimentations can be cast under a unifying software framework. We also introduce a class of algorithms for the distributed computation of statistical quantities, namely gossip algorithms, for which only two entities communicate and compute at each elementary step. We extend these algorithms be permitting k > 2 entities to interact on a per elementary step basis, simulate their behavior and propose protocols for implementing them.

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Books on the topic "Webpage ranking"

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Érdi, Péter. Ranking. Oxford University Press, 2019. http://dx.doi.org/10.1093/oso/9780190935467.001.0001.

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As humans, we like to see who is stronger, richer, better, or cleverer. As we also (1) love lists, (2) are competitive, and (3) are jealous of other people, we like ranking. We can rank some situations objectively: students ranked by their heights reflects objectivity. However, many “top-10” (or 21, 33, etc.) lists are based on subjective categorization and give only the illusion of objectivity. In fact, we don’t always want to be seen objectively since we don’t mind having a better image or rank than we deserve. The book applies scientific theories to everyday experience by raising and answering questions like: Are college ranking lists objective? How do we rank and rate countries based on their fragility, level of corruption, or even happiness? How do we find the most relevant webpages? How are employees ranked? The book is offered to people whose neighbor has a fancier car; employees who are being ranked by their supervisors; managers who are involved in ranking but may have qualms about the process; businesspeople interested in creating better visibility for their companies; scientists, writers, artists, and other competitors who would like to see themselves at the top of a success list; and college students who are just preparing to enter a new phase of social competition. Readers will engage in an intellectual adventure to better understand the difficulties of navigating between objectivity and subjectivity and to better identify and modify their place in real and virtual communities by combining human and computational intelligence.

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Book chapters on the topic "Webpage ranking"

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Lu, Peng, and Xiao Cong. "The Research on Webpage Ranking Algorithm Based on Topic-Expert Documents." In Advances in Intelligent Systems and Computing, 195–204. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-19024-2_20.

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Li, Yukun, Yunbo Ye, and Wenya Xu. "A Meta-Search Engine Ranking Based on Webpage Information Quality Evaluation." In Web and Big Data, 556–60. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-60290-1_46.

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Pyun, Gwangbum, and Unil Yun. "Ranking Techniques for Finding Correlated Webpages." In IT Convergence and Security 2012, 1085–95. Dordrecht: Springer Netherlands, 2012. http://dx.doi.org/10.1007/978-94-007-5860-5_130.

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Vraný, Jiří. "Parallel Algorithm for Query Content Based Webpages Ranking." In Business Information Systems, 85–96. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-01190-0_8.

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Swapna, B., and T. Anuradha. "Achieving Higher Ranking to Webpages Through Search Engine Optimization." In Proceedings of International Conference on Computational Intelligence and Data Engineering, 105–12. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-6319-0_9.

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Pyun, Gwangbum, and Unil Yun. "A Frequent Pattern Mining Technique for Ranking Webpages Based on Topics." In Lecture Notes in Electrical Engineering, 121–28. Dordrecht: Springer Netherlands, 2013. http://dx.doi.org/10.1007/978-94-007-6738-6_15.

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Puyalnithi, Thendral, and Madhu Viswanatham V. "Website Topology Modification with Hotlinks Using Mined Webusage Knowledge." In Advances in Data Mining and Database Management, 194–204. IGI Global, 2017. http://dx.doi.org/10.4018/978-1-5225-1877-8.ch012.

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The hotlinks are the special links introduced in the website to reduce the time to access certain webpages in a webpage that is present in the deeper levels of the topology. Hotlinks selection mechanism plays a vital role in quick access of webpages. The problem is to decide which webpage should be having hotlinks and where the hotlinks should be placed in the website tree topology. We have proposed a methodology which starts by finding the frequent webpage access pattern of visitors of the website. The frequent pattern is found using Associative mining, Apriori algorithm or Frequent Pattern Tree algorithm. Then the frequent patterns are passed through page ranking mechanism. We find the pattern which is having the highest priority. Then the hotlinks are created for the members (webpages hyperlinks) of the pattern. Thus, the work is about assigning hotlinks for a set of pages which are frequently visited. Thus, by updating the topology by introducing hotlinks we can reduce the time to access the web pages.

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"Chapter Three. Ranking Webpages by Popularity." In Google's PageRank and Beyond, 25–30. Princeton: Princeton University Press, 2006. http://dx.doi.org/10.1515/9781400830329-004.

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"Chapter Eleven. The HITS Method for Ranking Webpages." In Google's PageRank and Beyond, 115–30. Princeton: Princeton University Press, 2006. http://dx.doi.org/10.1515/9781400830329-012.

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"Chapter Twelve. Other Link Methods for Ranking Webpages." In Google's PageRank and Beyond, 131–38. Princeton: Princeton University Press, 2006. http://dx.doi.org/10.1515/9781400830329-013.

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Conference papers on the topic "Webpage ranking"

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Ganeshiya, Deepak Kumar, and Dilip Kumar Sharma. "A survey: hyperlink analysis in webpage ranking algorithms." In 2014 International Conference of Soft Computing Techniques for Engineering and Technology (ICSCTET). IEEE, 2014. http://dx.doi.org/10.1109/icsctet.2015.7371192.

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Ganeshiya, Deepak Kumar, and Dilip Kumar Sharma. "A novel approach for webpage ranking using updated content." In 2014 5th International Conference- Confluence The Next Generation Information Technology Summit. IEEE, 2014. http://dx.doi.org/10.1109/confluence.2014.6949383.

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Tiwari, Ankita, and Sushil Chaturvedi. "Optimized Technique for Ranking Webpage on Search Engine Optimization." In 2018 2nd International Conference on Micro-Electronics and Telecommunication Engineering (ICMETE). IEEE, 2018. http://dx.doi.org/10.1109/icmete.2018.00034.

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Venkataraman, Ganesh, and Arunkumar Ravichandran. "Adaptive Semantic Search: Re-Ranking of Search Results Based on Webpage Feature Extraction and Implicitly Learned Knowledge of User Interests." In 2014 Tenth International Conference on Semantics, Knowledge and Grids (SKG). IEEE, 2014. http://dx.doi.org/10.1109/skg.2014.22.

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Sameer, Venkata Udaya, and Rakesh Chandra Balabantaray. "Improving ranking of webpages using user behaviour, a Genetic algorithm approach." In 2014 International Conference on Networks & Soft Computing (ICNSC). IEEE, 2014. http://dx.doi.org/10.1109/cnsc.2014.6906674.

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Salminen, Joni, Juan Corporan, Roope Marttila, Tommi Salenius, and Bernard J. Jansen. "Using Machine Learning to Predict Ranking of Webpages in the Gift Industry." In icist 2019: 9th International Conference on Information Systems and Technologies. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3361570.3361578.

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