• 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%.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2006.06a
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• pp.354-360
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• 2006

The purpose of this paper is to propose a securely personalized context-aware cooperative query that supports a multi-level data abstraction hierarchy and conceptual distance metric among data values, while considering privacy concerns around user context awareness. The conceptual distance expresses a semantic similarity among data values with a quantitative measure, and thus the conceptual distance enables query results to be ranked. To show the feasibility of the methodology proposed in this paper we have implemented a prototype system in the area of site search in a large-scale shopping mall.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2009.05b
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• pp.175-179
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• 2009

Researches of current PMIS(Project Management Information System) information searching focus on providing personalized results as well as matching needed queries in an enormous amount of information. This paper aim at discovering hidden knowledge to provide personalized and inferred search results based on the ontology with categorized concepts and relations among construction data. The current PMIS searching occasionally presents too much redundant information or offers no matching results from large volumes of data. In this paper, we propose a service searching system, which becomes aware of users device using iPMIS(Intelligent Program Management Information System). And we design and plant the ontology-based iPMIS, which is aware of the context in its environment.

• Proceedings of the Korea Information Processing Society Conference
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• 2009.04a
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• pp.364-368
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• 2009

"소셜 네트워크(Social Network)와 검색(Search)의 만남"은 현재 인터넷 상에서 매우 의미 있는 두 영역의 결합이다. 이와 같은 두 영역의 결합을 통해 소셜 네트워크 내에서 친구들의 생각이나 관심사 및 활동을 검색하고 공유함으로써 검색의 효율성과 적합성을 높이기 위한 연구들이 활발히 수행되고 있다. 본 논문에서는 일반적인 소셜 관계 랭크(SRR : Social Relation Rank) 및 토픽이 반영된 소셜 관계 랭크(TS_SRR : Topic Sensitive_Social Relation Rank) 알고리즘을 제안한다. SRR은 소셜 네트워크 내에 존재하는 웹 사용자들의 내재적인 특성 및 검색 성향 등에 대한 관련성(또는 유사정도)을 수치로 산정한 '소셜 관계 지수(SRV : Social Relation Value)'에 랭킹(Ranking)을 부여한 것을 의미한다. 제안하는 알고리즘의 검색 적용 가능성을 검증하기 위해 첫째, 웹 사용자간 직접 또는 간접적인 연결로 구성된 소셜네트워크를 구성 한다. 둘째, 웹 사용자들의 속성에 내재된 정보를 이용하여 토픽별 SRV를 산정한 후 랭킹을 부여하고, 토픽별 변화되는 랭킹에 따라 소셜 네트워크를 재구성 한다. 마지막으로 (TS_)SRR과 웹 사용자들의 검색 패턴(Search Pattern)을 비교 실험 한다. 실험 결과 (TS_)SRR이 높은 웹 사용자 간에는 검색 패턴 또한 유사함을 확인 하였다. 결론적으로 (TS_)SRR 알고리즘을 기반으로 관심분야에 연관성이 높은, 즉 상위에 랭크 된 웹 사용자들을 검색하여 검색 패턴을 공유 또는 상속받는 다면 개인화 검색(Personalized Search) 및 소셜 검색(Social Search)의 효율성과 신뢰성 향상에 기여 할 수 있다.

• Journal of Digital Convergence
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• v.14 no.10
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• pp.311-317
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• 2016

In a smart commerce market, customers are seeking customized shopping services optimized for personal tastes and customized shopping experiences based on customers' tastes. Accordingly, individually customized services are needed more than anytime now. According to this demand, companies provide messaging services to the consumer, but do not include a personalized service for each individual. The purpose of this study is to combine the techniques of personalized services with messaging services and presents system construction plan and integrated model system for corporate messaging services and thereby possible are maximizing marketing effects and analyzing effects of cost savings. Applying the operation of the integrated model is proposed and message transmission scheme through effective personalization can be applied to personalized model using the personalized information according to customers' requirements. The integrated model of personalization on this study is expected to be highly effective when combined with the search service.

• Journal of Broadcast Engineering
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• v.12 no.4
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• pp.321-332
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• 2007

The digitalization of a broadcast has caused the oversupply of the contents in order to fit the user's needs for various broadcast services. The massive contents could not help demanding one-sided watching without considering a taste and preference of a user. This situation is enlarging the demand about the personalized broadcast service to enable user to watch broadcast contents in anytime according to surplus provision of broadcast contents. TV-Anytime standard could be a solution for broadcast service to enable users to watch personalized broadcast contents according to their preference. The paper proposes the personalized broadcasting system for authoring, receiving and transmission of TV-Anytime metadata, the detailed information about the broadcast contents so that user could efficiently search a large of broadcast contents stored in the PDR(Personal Digital Recorder)receiver under the DCATV(Digital Cable Television) environment.

• Journal of Korea Multimedia Society
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• v.19 no.11
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• pp.1878-1890
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• 2016

Web 2.0 has features produced the content through the user of the participation and share. The content production activities have became active since social network service appear. The social bookmark, one of social network service, is service that lets users to store useful content and share bookmarked contents between personal users. Unlike Internet search engines such as Google and Naver, the content stored on social bookmark is searched based on tag keyword information and unnecessary information can be excluded. Social bookmark can make users access to selected content. However, quick access to content that users want is difficult job because of the user of the participation and share. Our paper suggests a method recommending search word to be able to access quickly to content. A method is suggested by using Collaborative Filtering and Jaccard similarity coefficient. The performance of suggested system is verified with experiments that compare by 'Delicious' and "Feeltering' with our system.

• ETRI Journal
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• v.29 no.5
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• pp.545-558
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• 2007

This paper presents an efficient semantic service discovery scheme called UbiSearch for a large-scale ubiquitous computing environment. A semantic service discovery network in the semantic vector space is proposed where services that are semantically close to each other are mapped to nearby positions so that the similar services are registered in a cluster of resolvers. Using this mapping technique, the search space for a query is efficiently confined within a minimized cluster region while maintaining high accuracy in comparison to the centralized scheme. The proposed semantic service discovery network provides a number of novel features to evenly distribute service indexes to the resolvers and reduce the number of resolvers to visit. Our simulation study shows that UbiSearch provides good semantic searchability as compared to the centralized indexing system. At the same time, it supports scalable semantic queries with low communication overhead, balanced load distribution among resolvers for service registration and query processing, and personalized semantic matching.

• The Journal of the Korea Contents Association
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• v.17 no.2
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• pp.113-124
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• 2017

The existing social search has a problem that search results are not suitable for a user since it does not take into account the recency of the user profile and the interests of similar users. Therefore, studies on a social search considering a temporal attribute and the interests of other users are required. In this paper, we propose a social search scheme that takes into account the recent interests of a user by time and the interests of the most similar users. The proposed scheme analyzes the activity information of a social media user in order to take into account the recent interests of the user. And then the proposed scheme improves the satisfaction and accuracy of search results by combining the interests of similar users with the analyzed information and performing ranking, It is shown through performance evaluation that the proposed scheme outperforms the existing scheme.

• Proceedings of the Korean Information Science Society Conference
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• 2003.04c
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• pp.498-500
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• 2003

인터넷의 급속한 팽창으로 인해 가을 정보의 양이 폭발적으로 증가하고 있다. 웹 사용자에게 이용 가치가 없는 정보 범람(information overflow)안이 발생한다면 효율적인 정보검색이 되지 못하므로 사용자가 원하는 정보만을 얻을 수 있다면 시간과 미숙한 정보의 검색을 방지 할 수 있다. 본 논문에서는 한국어 질의 생성과 관련하여 웹 사용자의 편의성과 효율성을 고려한 한국어 질의 처리 방법론과 개인형 메타 검색 모델을 제안하고자 한다. 한국어 질의를 기본으로 하여 한국어 문장 패턴 및 개인 정보 평가 구성 요소를 이용한 방법론과 모델을 제안하고자 한다.