A Web Contents Ranking System using Related Tag &amp; Similar User Weight

Park, Su‐Jin; Lee, Si-Hwa; Hwang, Dae-Hoon

doi:10.9717/kmms.2011.14.4.567

Cited by 2 publications

(1 citation statement)

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“…Several methods such as collaborative tagging [1], hierarchical structure of tags [2], tag-based searches and ranking [3][4], and tag-based recommendation [5] have been developed. However, most of the existing tag-based methods use only the tag coappearing frequency to find the tag-pair semantic similarity; hence, their result of is inaccurate in the translation.…”

Section: Introductionmentioning

confidence: 99%

Comparison and Evaluation of Highly Related Tag-Pairs Extraction Methods

Hwang

2015

IJMUE

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Until recently, it was expected that the web users' tagging would be used in classification, recommendation, and information search. However, the users' subjective decision or infrequent tags with high interrelationship have caused inaccurate results.This research proposes an algorithm to disregard users' subjective tags and utilize infrequent tags with high semantic similarity using WordNet. Furthermore, to enhance the efficiency of the proposed algorithm, a weighted matrix is proposed.To evaluate the proposed algorithm, the current co-appearing frequency between tagpairs method, tag-pair semantic similarity extraction algorithm, and tag-pair weight matrix method were analyzed and compared.

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Section: Introductionmentioning

confidence: 99%

Comparison and Evaluation of Highly Related Tag-Pairs Extraction Methods

Hwang

2015

IJMUE

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Customized Web Search Rank Provision

Kang¹,

Bae²

2013

Journal of Korean Institute of Industrial Engineers

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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. †

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A Web Contents Ranking System using Related Tag & Similar User Weight

Cited by 2 publications

References 1 publication

Comparison and Evaluation of Highly Related Tag-Pairs Extraction Methods

Comparison and Evaluation of Highly Related Tag-Pairs Extraction Methods

Customized Web Search Rank Provision

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