Comparative study of different ranking algorithms adopted by search engine

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Comparative study of different ranking algorithms adopted by search engine

Comparative study of different ranking algorithms adopted by search engine

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  • 1. COMPARATIVE STUDY OF DIFFERENT RANKING ALGORITHMS ADOPTED BY SEARCH ENGINE Under the guidance of , Dr. Manoj Wadhwa Presented by, Shikha Taneja 12-MCS-110
  • 2. MOTIVATION  When searching for information on the WWW, user perform a query to a search engine. The engine return, as the query’s result, a list of Web sites which usually is a huge set. So the ranking of these web sites is very important. Because much information is contained in the link-structure of the WWW, information such as which pages are linked to others can be used to augment search algorithms.  It is so important for any web search engine to rank the pages with the aim of providing more useful data, by listing the pages containing the data at higher places, to the searcher about the searched keyword or subject.
  • 3.  So to be able to provide desired ordering for the web pages: A page ranking algorithm is the technique to rank websites in their search engine results.  Together with the development of the Internet and the popularity of World Wide Web, Web page ranking systems have drawn significant attention.  Many Web Search Engines have been introduced until now, but still have difficulty in providing completely relevant answers to the general subject of queries.  The main reason is not the lack of data but rather an excess of data.
  • 4. WHAT IS SEARCH ENGINE?? Web Search Engine is a tool enabling document search, with respect to specified keywords, in the Web and returns a list of documents where the keywords were found.
  • 5. INTRODUCTION  Early search engines mainly compare content similarity of the query and the indexed pages.  From 1996, it became clear that content similarity alone was no longer sufficient.  The number of pages grew rapidly in the mid-late 1990’s.  Content similarity is easily spammed.  A page owner can repeat some words and add many related words to boost the rankings of his pages and/or to make the pages relevant to a large number of queries.  Starting around 1996, researchers began to work on the problem. They resort to hyperlinks.
  • 6.  Web pages on the other hand are connected through hyperlinks, which carry important information.  Some hyperlinks: organize information at the same site.  Other hyperlinks: point to pages from other Web sites.  Those pages that are pointed to by many other pages are likely to contain authoritative information.  During 1997-1998, two most influential hyperlink based search algorithms PageRank and HITS were reported.
  • 7. PAGE RANK     PageRank is an algorithm used by the Google web search engine to rank websites in their search engine results.  PageRank works by counting the number and quality of links to a page to determine a rough estimate of how important the website is. The underlying assumption is that more important websites are likely to receive more links from other websites. It is an excellent way to prioritize the result of web keyword searches. Example of the PageRank indicator as found on the Google toolbar:
  • 8. HITS ALGORITHM      The HITS algorithm stands for “Hypertext Induced Topic Selection” and is used for rating and ranking websites based on the link information when identifying topic areas. Unlike PageRank which is a static ranking algorithm, HITS is search query dependent. It is a very popular and effective algorithm to rank documents based on the link information among a set of documents. An authority value is computed as the sum of the scaled hub values that point to that page. A hub value is the sum of the scaled authority values of the pages it points to.
  • 9. When the user issues a search query,  HITS first expands the list of relevant pages returned by a search engine and then produces two rankings of the expanded set of pages, authority ranking and hub ranking. Authority: Roughly, a authority is a page with many in-links.  The idea is that the page may have good or authoritative content on some topic and  thus many people trust it and link to it. Hub: A hub is a page with many out-links.  The page serves as an organizer of the information on a particular topic and  points to many good authority pages on the topic. 
  • 10. EXAMPLES
  • 11. SALSA    SALSA- The Stochastic Approach for Link- Structure Analysis (Lempel, Moran 2001)  Probabilistic extension of the HITS algorithm  Combines ideas from both HITS and PAGERANK  Random walk is carried out by following hyperlinks both in the forward and in the backward direction SALSA uses authority and hub score SALSA creates a neighborhood graph using authority and hub pages and links
  • 12. WEIGHTED PAGERANK ALGORITHM     Weighted Page Rank algorithm is an extension of the PageRank algorithm. This algorithm allocates a higher rank values to the more significant pages rather than dividing the rank value of a page evenly among its outgoing linked web pages. Each outgoing link gets a value proportional to its significance. WPR takes into account the importance of both the inlinks and outlinks of the pages and distributes rank scores based on the popularity of the pages.
  • 13. DISTANCE RANK ALGORITHM,     The distance between pages is considered as a factor. The algorithm calculates the minimum average distance between two web pages and more pages. This adopts the Page-Rank properties i.e. the rank of each page is computed as the weighted sum of ranks of all incoming pages to that particular page. Then, a page has a high page rank value if it has more incoming links on a page.
  • 14. TOPIC SENSITIVE PAGE-RANK ALGORITHM This algorithm computes the scores of web page according to the importance of content available on web page.  Pages receiving only a few incoming links, but from very related web sites, will be given much more consideration for that topic. The result will be a higher Topic-Sensitive Page Rank for that site, for that specific search query, despite a lower Page Rank under the current system 
  • 15. COMPARISON BETWEEN DIFFERENT SEARCH ENGINES CRITERI PAGERA HITS A NK SALSA Weighted Distance PageRank Rank TopicSensitive PageRank Came into 1998 existence 1999 2001 2006 1998 2000 Objective to rank document s based on the link informatio n among a set of document s. Perform a random walk alternatin g between hubs and authoritie s Weight of web page is calculated on the basis of inbound and outbound links and on the basis of weight of The algorithm calculates the minimum average distance between two web pages and more pages. This algorithm computes the scores of web page according to the importanc e of content available on web an excellent way to prioritize the result of web keyword searches
  • 16. CRITERIA PAGERAN HITS  K SALSA Weighted PageRank Distance Rank TopicSensitive PageRank Input parameters Back links Content, Back and Forward links Content, Back links and forward links Back links and forward links Inbound links Content, Back link, Forward Link Importance High. Back links are considered. Moderate. Hub & authorities scores are utilized. High. it weighs the entries according to their in and out-degrees. High. The pages are sorted according to the importance. High. It is based on distance between the pages. High. It computes important score per topic. Limitations Query independent, Dangling page Topic drift and efficiency problem Query dependent, handle spam but not as good as PageRank Query independent, Dangling page Needs to work along with Page-Rank Only available to text, images are not taken into account. Search Engine Google Clever Google Research model Research Model Google Quality Of Results Medium Less than Page Rank Less than Page Rank Higher than Page Rank Less than Page-Rank High
  • 17. PROPOSED WORK  The proposed work in the Page Rank algorithm includes the implementation to solve the problem of Dangling Page. Dangling pages are pages which do not have any outbound link or the page which does not provide any reference to other pages. These Dangling pages create many issues to calculate efficient page rank of different pages of a websites .
  • 18. REFERENCES o Mridula Batra, Sachin Sharma, “Comparative Study Of Page rank algorithm with different ranking algorithms adopted by search engine for website ranking” , Int.J.Computer Technology & Applications,Vol 4 (1), 8-18, Jan-Feb 2013 o Ankur gupta, Rajni Jindal, “An overwiew of ranking algorithm for search engines”, INDIAcom-2008CFND, Feb 08-09,2008  Alessio Signorini, “A Survey of Ranking Algorithms”, Department of Computer Science University of Iowa, September 11, 2005  Mitali Desai, Sanjaysinh Parmar, Nitesh Shah, Jitendra Upadhyay, “A Study of different Page Rank Algorithms: Issues”, International Journal of Computer Science Research & Technology, ISSN: 2321-8827 IJCSRTIJCSRT www.ijcsrt.org IJCSRTV1IS040089 Vol. 1 Issue 4, September - 2013 o Sergey Brin and Lawrence Page, “The Anatomy of a Large-Scale Hypertextual Web Search Engine”  Marc Najork, “Comparing the Effectiveness of HITS and SALSA”, Microsoft Research, 1065 La Avenida, Mountain View, CA 94043, USA, najork@microsoft.com. o Dilip Kumar Sharma, A.K.Sharma, ”A Comparative Analysis Of Web Page Ranking Algorithms” in proceedings of the International Journal Computer Science and Engineering,Vol. 02,No. 08,2010,2670-2676.  R. lempel and S. moran, “SALSA: The Stochastic Approach for Link-Structure Analysis”  Allan borodin, Gareth o. roberts, Ieffrey s. rosenthal and Panayiotis tsaparas, “Link Analysis Ranking: Algorithms, Theory, and Experiments”