• The KIPS Transactions:PartD
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• v.9D no.5
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• pp.745-754
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• 2002

The Google search site (http://www.google.com), which was introduced in 1998, implemented the PageRank algorithm for the first time. PageRank is a ranking method based on the link structure of the Web pages. Even though PageRank has been implemented and being used in various commercial search engines, implementation details did not get documented well, primarily due to business reasons. Implementation techniques introduced in [4,8] are not sufficient to produce PageRank values of Web pages. This paper explains the techniques[4,8], and suggests major data structure and four implementation techniques in order to apply the PageRank algorithm. The paper helps understand the methods of applying PageRank algorithm by means of showing a real system that produces PageRank values of Web pages.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.46 no.5
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• pp.14-28
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• 2009

The PageRank algorithm is an important component for ranking Web pages in Google and other search engines. While many improvements for the original PageRank algorithm have been proposed, it is unclear which variations (and their combinations) provide the "best" ranked results. In this paper, we evaluate the ranking quality of the well-known variations of the original PageRank algorithm and their combinations. In order to do this, we first classify the variations into link-based approaches, which exploit the link structure of the Web, and knowledge-based approaches, which exploit the semantics of the Web. We then propose algorithms that combine the ranking algorithms in these two approaches and implement both the variations and their combinations. For our evaluation, we perform extensive experiments using a real data set of one million Web pages. Through the experiments, we find the algorithms that provide the best ranked results from either the variations or their combinations.

• The KIPS Transactions:PartB
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• v.18B no.4
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• pp.231-240
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• 2011

Many search algorithms have been implemented by many researchers on the world wide web. One of the best algorithms is Google using PageRank technology. PageRank approach computes the number of inlink of each documents then ranks documents in the order of inlink members. But it is difficult to find the results that user needs, because this method find documents not valueable for a person but valueable for the public. To solve this problem, We use the WordNet for analysis of the user's query history. This paper proposes a personalized search engine using the user's query history and PageRank Check. We compared the performance of the proposed approaches with google search results in the top 30. As a result, the average of the r-precision for the proposed approaches is about 60% and it is better as about 14%.

• Journal of KIISE:Databases
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• v.33 no.7
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• pp.708-714
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• 2006

The World Wide Web has become an entrenched global medium for storing and searching information. Most people begin at a Web search engine to find information, but the user's pertinent search results are often greatly diluted by irrelevant data or sometimes appear on target but still mislead the user in an unwanted direction. One of the intentional, sometimes vicious manipulations of Web databases is Web spamming as Google bombing that is based on the PageRank algorithm, one of the most famous Web structuring techniques. In this paper, we regard the Web as a directed labeled graph that Web pages represent nodes and the corresponding hyperlinks edges. In the present work, we define the label of an edge as having a link context and a similarity measure between link context and the target page. With this similarity, we can modify the transition matrix of the PageRank algorithm. A motivating example is investigated in terms of the Singular Value Decomposition with which our algorithm can outperform to filter the Web spamming pages effectively.

• Journal of Information Management
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• v.37 no.2
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• pp.33-50
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• 2006

In this paper, after reviewing hyperlink based ranking methods, we saw various other parameters that effect ranking. Then, We analyzed the PageRank and HITS(Hypertext Induced Topic Search) algorithm, which are two popular methods that use eigenvector computations to rank results in terms of their characteristics. Finally, google and Ask.com search engines were examined as examples for applying those methods. The results showed that use of Hyperlink structure can be useful for efficiency of web site search.

• 한국HCI학회:학술대회논문집
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• 2008.02a
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• pp.683-687
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• 2008

Many search algorithms have been implemented by many researchers on the world wide web. One of the best algorithms is Google using PageRank technology, PageRank approach, computes the number of inlink of each documents then represents documents in order of many inlink. But It is difficult to find the results that user needs. Because this method finds documents not valueable for a person but valueable for public, this paper propose a personalized search engine mixed public with personal worth to solve this problem.

• Journal of the Korea Society of Computer and Information
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• v.10 no.1 s.33
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• pp.59-66
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• 2005

The organization of the web is progressively more being used to improve search and analysis of information on the web as a large collection of heterogeneous documents. Most people begin at a Web search engine to find information. but the user's pertinent search results are often greatly diluted by irrelevant data or sometimes appear on target but still mislead the user in an unwanted direction. One of the intentional, sometimes vicious manipulations of Web databases is a intentionally biased web page like Google bombing that is based on the PageRank algorithm. one of many Web structuring techniques. In this thesis, we regard the World Wide Web as a directed labeled graph that Web pages represent nodes and link edges. In the Present work, we define the label of an edge as having a link context and a similarity measure between link context and target page. With this similarity, we can modify the transition matrix of the PageRank algorithm. By suggesting a motivating example, it is explained how our proposed algorithm can filter the Web intentionally biased web Pages effective about $60\%% rather than the conventional PageRank.

• Journal of Korean Institute of Industrial Engineers
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• v.39 no.2
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• pp.119-128
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• 2013

Most internet users utilize internet portal search engines, such as Naver, Daum and Google nowadays. But since the results of internet portal search engines are based on universal criteria (e.g. search frequency by region or country), they do not consider personal interests. Namely, current search engines do not provide exact search results for homonym or polysemy because they try to serve universal users. In order to solve this problem, this research determines keyword importance and weight value for each individual search characteristics by collecting and analyzing customized keyword at external database. The customized keyword weight values are integrated with search engine results (e.g. PageRank), and the search ranks are rearranged. Using 50 web pages of Goolge search results for experiment and 6 web pages for customized keyword collection, the new customized search results are proved to be 90% match. Our personalization approach is not the way that users enter preference directly, but the way that system automatically collects and analyzes personal information and then reflects them for customized search results.

• Journal of Information Technology Applications and Management
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• v.13 no.4
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• pp.167-180
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• 2006

Websites in the Internet are uncontrollable domain and various contents in websites lead people's activities and thoughts and new business paradigms for the future. These phenomena are from expanding the social network based on the endless growth of information technology. Websites are composed with many of links and communicating and expanding their virtual area by links, inbound, outbound, onsite, and of offsite links. Research and practice in digital information on the web have focused on finding and measuring artifacts, factors and attributes of web structure and contents from the perspective that information is a resource and property of products and services. Websites links is one of the core artifacts for understanding the virtual area. This study identifies the role of web link structure and webpage keyword as artifacts and examines their relationships by webpage rank by a minimal hub as performance in the business websites that are serving tourism information. Discovering relationships of links provides managerial insights on organizations virtual activities and systematic understandings about digitalized organizational information in the information use environment.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.4 no.3
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• pp.288-291
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• 2004

This paper proposes an intelligent search modeling using avatar agent. This system consists of some modules such as agent interface, agent management, preprocessor, interface machine. Core-Symbol Database and Spell Checker are related to the preprocessor module and Interface Machine is connected with Best Aggregate Designer. Our avatar agent system does the indexing work that converts user's natural language type sentence to the proper words that is suitable for the specific branch information retrieval. Indexing is one of the preprocessing steps that make it possible to guarantee the specialty of user's input and increases the reliability of the result. It references a database that consists of synonym and specific branch dictionary. The resulting symbol after indexing is used for draft search by the internet search engine. The retrieval page position and link information are stored in the database. We experimented our system with the stock market keyword SAMSUNG_SDI, IBM, and SONY and compared the result with that of Altavista and Google search engine. It showed quite excellent results.