Using Web Archive for Improving Search Engine Results (original) (raw)
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Computation of Ranking of Web Pages
In this paper, we present Google, a prototype of a large-scale search engine which makes heavy use of the structure present in hypertext. Google is designed to crawl and index the Web efficiently and produce much more satisfying search results than existing systems.This paper serves as a companion or extension to the " Inside Page Rank " , Everyday we Google out something to found out the desire content, and upto this time, nearly half of the world just do Google on the Internet. Well in this paper we are concerned with the mechanism behind the working of PageRank Algorithm, i.e how the ranking of pages is done, how the PageRank is computed, how the in-links and out-links affects the ranking of pages. We introduce a few new results, provide an extensive reference list, and speculate about exciting areas of future research.
Exploration of Several Page Rank Algorithms for Link Analysis
International journal of engineering research and technology, 2014
Web is the largest collection of information and it is growing continuously. Pages& Documents are added and deleted on frequent basis due to dynamic nature of the web. The web is serving as the major source of meaningful information related to query made by the user. The search engine applies different algorithms for link analysis to fetch most relevant pages and documents which are presented at the top of the result list. To assist the users to navigate in the result list, ranking methods are applied on the search results. Most of the ranking algorithms proposed in the literature are PageRank (PR) [1], Weighted PageRank (WPR) [5], Hyperlink-Induced Topic Search (HITS) [4], Page Ranking Algorithm Based On Number Of Visits Of Links Of Web Page [7],An Improved Page Ranking Algorithm Based On Optimized Normalization Technique [6],Improved method for computation of PageRank[8], Weighted Page Ranking Algorithm Based On Number Of Visits Of Links Of Webpage [9].With the understanding that ...
Proceedings of the 15th international conference on World Wide Web - WWW '06, 2006
Since the publication of Brin and Page's paper on PageRank, many in the Web community have depended on PageRank for the static (query-independent) ordering of Web pages. We show that we can significantly outperform PageRank using features that are independent of the link structure of the Web. We gain a further boost in accuracy by using data on the frequency at which users visit Web pages. We use RankNet, a ranking machine learning algorithm, to combine these and other static features based on anchor text and domain characteristics. The resulting model achieves a static ranking pairwise accuracy of 67.3% (vs. 56.7% for PageRank or 50% for random).
IJERT-A Comparative Study of Various Page Ranking Algorithms
International Journal of Engineering Research and Technology (IJERT), 2014
https://www.ijert.org/a-comparative-study-of-various-page-ranking-algorithms https://www.ijert.org/research/a-comparative-study-of-various-page-ranking-algorithms-IJERTV3IS11182.pdf With the rapidly expanding Web, users get disoriented in the rich hyper structure. To provide the internet users with relevant information to satisfy their requirements is the main goal of website owners. Therefore, it is very crucial to find and retrieve the relevant data on the Internet and also find out the user's interests and needs from their behavior. When a user enters a query in a search engine, quite a large number of pages are generally referred in response to user's query. To aid users to navigate in the search result list, various ranking methods are applied on the search results.
An Improved Approach to Ranking Web Documents
Ranking thousands of web documents so that they are matched in response to a user query is really a challenging task. For this purpose, search engines use different ranking mechanisms on apparently related resultant web documents to decide the order in which documents should be displayed. Existing ranking mechanisms decide on the order of a web page based on the amount and popularity of the links pointed to and emerging from it. Sometime search engines result in placing less relevant documents in the top positions in response to a user query. There is a strong need to improve the ranking strategy. In this paper, a novel ranking mechanism is being proposed to rank the web documents that consider both the HTML structure of a page and the contextual senses of keywords that are present within it and its back-links. The approach has been tested on data sets of URLs and on their back-links in relation to different topics. The experimental result shows that the overall search results, in response to user queries, are improved. The ordering of the links that have been obtained is compared with the ordering that has been done by using the page rank score. The results obtained thereafter shows that the proposed mechanism contextually puts more related web pages in the top order, as compared to the page rank score.
On the Improvement of Weighted Page Content Rank
Journal of Advances in Computer Networks, 2013
The World Wide Web has become one of the most useful information resource used for information retrievals and knowledge discoveries. However, Information on Web continues to expand in size and complexity. Making the retrieval of the required web page on the web, efficiently and effectively, is a challenge. Web structure mining plays an effective role in finding or extracting the relevant information. In this paper we proposed a new algorithm, the Simplified Weighted Page Content Rank (SWPCR) for page rank, based on combination of two classes of Web mining "Web structure mining" and "Web content mining". This algorithm will be an enhancement to the well-known Page Rank algorithm by adding to this algorithm a content weight factor (CWF) to retrieve more relevant page.
Nascent Weighted Page Rank Algorithm
International Journal of Computer Applications, 2017
There may be a millions of web pages that include a particular words or specific phrases. However some of them will be more relevant and popular than others. Modern search engines apply methods of ranking the results to present the best results first after that just plain text searching. The main objective of this paper is to explain the various existing page ranking algorithms and the enhancement done to the standard page rank algorithm. The weighted page rank algorithm based on visits of links by user is enhanced and a new algorithm called Nascent Weighted Page Rank (NWPR) algorithm is proposed. The proposed algorithm considers the additional factor of weight due to outlinking pages in spite of weight due to inlinking pages and the visits of links by user in calculating the page rank. It is observed that the results of the proposed algorithm are comparable to the previously known algorithms. Also the value of page ranks of web pages computed by the NWPR is largely dependent on the value of d (damping factor).
Comparative Study of Web Page Ranking Algorithms
With the exponential growth of information on web, getting relevant information regarding user query through search engines is a tedious job today. Several search engines use link analysis algorithms to rank the web pages according to the need. But these algorithms are still lacking with efficiency, scalability and relevancy issues. This paper put forward survey of various improved ranking algorithms and their pros and cons. Further, we have included comparative study of various ranking algorithms mainly PageRank and HITS based on computation environments like Sequential, Parallel. This will help scientist, researchers, and academicians working in this area to understand the existing algorithms and develop one which is need of today’s environment.