• 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 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 Information Processing Systems
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• v.9 no.2
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• pp.217-236
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• 2013

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.

• Journal of KIISE:Computing Practices and Letters
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• v.16 no.1
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• pp.100-104
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• 2010

In the past decade, various search engines have been developed to retrieve web pages that web surfers want to find from world wide web. In search engines, one of the most important functions is to evaluate and rank web pages for a given web surfer query. The prior algorithms using hyperlink information like PageRank incur the problem of 'topic drift'. To solve the problem, relevance propagation models have been proposed. However, these models suffer from serious performance degradation, and thus cannot be employed in real search engines. In this paper, we propose a new ranking algorithm that alleviates the topic drift problem and also provides efficient performance. Through a variety of experiments, we verify the superiority of the proposed algorithm over prior ones.

• International Journal of Computer Science & Network Security
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• v.21 no.7
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• pp.267-272
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• 2021

The current study aimed at measuring the rate of loss in search results of the actual number of publications in journals indexed by Web of Science when not using the accurate official authority name as indicated by the Ministry of Education. Conducting a search using the authority name does not always yield complete results of all existing publications. Researchers in Saudi universities tend to use up to 10 different random names of universities when searching. This interesting fact has prompted the authors of this paper to conduct a study on the search results of 30 Saudi universities using the authority name as indicated by the Ministry of Education. The statistical analyses revealed that there is a high tendency for the wrong use of authority names. Results show that 8 universities were not found in the search results. Furthermore, other universities are losing between 10 and 30% of search results that reflect the actual number of publications. Consequently, the rank of each university, as well as the general rank of Saudi universities in the Web of Science, will be affected.

• 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 Journal of Korean Institute of Communications and Information Sciences
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• v.38B no.5
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• pp.385-393
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• 2013

In the past decade, a paradigm shift from machine-centered to human-centered and from technology-driven to user-driven has been witnessed. Consequently, Social search is getting more social and Social Network Service (SNS) is a popular Web service to connect and/or find friends, and the tendency of users interests often affects his/her who have similar interests. If we can track users' preferences in certain boundaries in terms of Web search and/or knowledge sharing, we can find more relevant information for users. In this paper, we propose a novel Topic Sensitive_Social Relationship Rank (TS_SRR) algorithm. We propose enhanced Web searching idea by finding similar and credible users in a Social Network incorporating social information in Web search. The Social Relation Rank between users are Social Relation Value, that is, for a different topics, a different subset of the above attributes is used to measure the Social Relation Rank. We observe that a user has a certain common interest with his/her credible friends in a Social Network, then focus on the problem of identifying users who have similar interests and high credibility, and sharing their search experiences. Thus, the proposed algorithm can make social search improve one step forward.

• 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.

• Journal of the Korean association of regional geographers
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• v.13 no.3
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• pp.290-300
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• 2007

In this study, web document frequency of city place name is analyzed and it is used as the dataset for rank-size analysis. The search keywords are compared in the context of spatial meaning and the different domain corpus is applied. The acquired search results are applied for the further analysis. Firstly, the rank-size analysis is applied to compare the result between population and document frequency. Secondly, in case of correlation analysis, the significant changes are revealed when the spatial criteria for search keywords are increased. In case of corpus, COM, NET, and ORG shows the higher coefficient values. Lastly, the cluster analysis is applied to classify the list of cities that shows the similarity and difference. These analyses have a significant role in representing the rank-size distribution of city names that are reflected on the web documents in the information society.