• Journal of the Korean Society for information Management
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• v.31 no.2
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• pp.79-97
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• 2014

This study was to evaluate the quality of academic information services provided by Naver Academic Information Service, Google Scholar, and MS Academic Search. This academic information services were evaluated in terms of the contents, service, and effectiveness. 135 four year college students were recruited for the survey. The results showed that the Google Scholar in contents section had higher score than Naver and MS Academic Search. In regard to service, Google Scholar had higher score in retrieval section while Naver had higher score in design section respectively. Finally, both Google Scholar and Naver in the access section had higher score than MS Academic Search.

• Journal of the Korean Society for Library and Information Science
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• v.51 no.2
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• pp.255-279
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• 2017

In a qualitative approach, this study investigated users' perceptions associated with their satisfactions in the process of using the scholarly resource search functions of the academic version of the NAVER search engine. For this study, the data was collected from a group of undergraduate students, who conducted academic information searches in the field of own major disciplinary areas, using the Web portal. Based on the data, students' satisfactions and dissatisfactions along with the reasons of their perceptions were analyzed. The results presented users' perceptions in various evaluation criteria based on the three major domains: system interfaces, retrieval mechanisms and search results. Based on the results, the study proposed the following suggestions: 1) the enhancements of the system interfaces and HELP guidances based the limited user knowledge on basic system terminologies 2) the improvements of the retrieval mechanisms associated with understanding the contexts of the search terms presented by users 3) the necessity of the user education due to the insufficient user knowledge of the retrieval mechanisms and the search functions.

• Proceedings of the Korean Society of Computer Information Conference
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• 2023.07a
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• pp.227-228
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• 2023

본 눈문에서는 안드로이드 플랫폼 기반의 스마트폰에서 장애 예술인 활동에 도움이 되는 애플리케이션을 설계 및 구현하였다. 이 애플리케이션의 특징은 TTS, STT 기능을 이용한다. STT 기능은 청각 장애인이 예술 작품을 올리면 작품에 대한 정보를 입력받고, TTS 활용하여 작품에 대한 설명을 음성으로 서비스하도록 구현한다. Naver Map을 사용하여 사용자가 전시회에 대한 위치를 등록하면 Naver Map을 통하여 전시회를 찾아올 수 있도록 길 안내 서비스를 구현한다.

• Journal of the Korean Society for information Management
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• v.30 no.1
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• pp.33-50
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• 2013

The purpose of this study was to identify differences between KCI and Naver Scholar as citation analysis tools. Four subcategories in the humanities category were selected as the subject of study. The recall of Naver Scholar was 64%(2,227 times) and the KCI's was 77%(2,665 times). There were some differences in the results at the individual article level or the subcategory level, but the gaps were not significant. Therefore, researchers who analyze citations are urged to use both databases because neither of them are complete, but supplementary to each other.

• Proceedings of the Korean Society of Computer Information Conference
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• 2023.07a
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• pp.441-442
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• 2023

본 논문에서는 GPS 센서, Naver Map API, Geocoder를 사용하여 안드로이드 플랫폼 기반의 스마트폰에서 실행되는 'HealthSchedule' 애플리케이션을 설계하고 구현한다. 이 애플리케이션에서는 운동 소요시간, 운동 종목, 날짜, 도착 위치를 입력하여 등록한 후, 해당 스케줄을 실행하여 사용자의 실시간 이동 좌표를 표시하고, 이동 거리, 전체 및 현재 속도, 운동 종목별 소모 칼로리 등을 출력한다.

• Proceedings of the Korean Society of Computer Information Conference
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• 2022.01a
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• pp.237-238
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• 2022

본 연구에서는 언택트 시대에 여행 트렌드가 된 차박을 위한 모바일 앱 'GO차박'을 설계하고 구현하였다. 코로나 19로 인해 언택트 문화가 새로운 트렌드로 자리 잡게 되면서 '차박'에 대한 관심이 급격하게 증가하게 되었다. 이에 따라 차박에 대한 정보를 찾는 수요자와 정보는 증가하였으나 방대한 정보에 대한 용이한 접근을 제공하고 증가하는 수요자를 만족시킬 수 있는 어플리케이션은 미흡하였다. 따라서 본 연구에서는 많은 차박 수요자들에게 쉽게 접근하여 차박에 대한 정보를 종합적이고 간편하게 찾아볼 수 있고 서로 정보 공유도 할 수 있는 모바일 앱을 제공하고자 'GO차박'을 개발하게 되었다. 방대한 정보를 다루기 때문에 깔끔한 UI를 구성하였고 Firebase와 NaverSearch API, Naver Map API, OpenWeatherAPI를 사용하여 정보의 정확도를 높이고자 하였다. 본문에서는 차박 앱에 대한 주요 기능들을 설명하고 결론에서는 'GO차박'의 기대 효과와 향후 확장 방향에 대해 제시하고 있다.

• Journal of Information Management
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• v.36 no.4
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• pp.71-89
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• 2005

This paper examines the ways of using information resources available through information retrieval systems of portal sites. We analyze the types of information resources, search capabilities, and interfaces of Naver, Empas, and Google Scholar. Naver's retrieval system sells research reports, papers, patents information, etc. to users, which is similar to C2C(Customer to Customer in e-commerce environment). Empas provides information from journals, research reports, and proceedings with no charge. Google Scholar's noteworthy efforts are their collaborative programs with and/or for major U.S. libraries, such as "Library Link" and "Library Project." Considering the extended information retrieval services of portals, especially the services like Google Scholar's library programs, libraries need to develop more specialized services, such as the customized information service for individual user, development of user convenience tools like OCLC WorldCat, more accessibility through ubiquitous library concept, and collaboration among libraries.

• Journal of the Korean Society for information Management
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• v.30 no.2
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• pp.297-315
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• 2013

This study aims to analyze and evaluate term suggestion services of major search portals, Naver and Google Korea. In particular, this study evaluated relevance and currency of related search terms provided, and analyzed characteristics such as number and distribution of terms, and queries that did not produce terms. This study also analyzed types of terms in terms of the relationship between queries and terms, and investigated types and characteristics of harmful terms and terms with grammatical errors. Finally, Korean queries and English queries, and popular queries and academic queries were compared in terms of the amount and relevance of search terms provided. The results of this study show that the relevance and currency of Naver's related search terms are somewhat higher than those of Google. Both Naver and Google tend to add terms to or delete terms from original queries, and provide identical search terms or synonym terms rather than providing entirely new search terms. The results of this study can be implemented to the portal's effective development of term suggestion services.

• Journal of Convergence for Information Technology
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• v.12 no.3
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• pp.116-125
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• 2022

The purpose of this study is to analyze whether investor anxiety caused by COVID-19 affects cryptocurrency prices in the COVID-19 pandemic, and to experiment with cryptocurrency price prediction based on a deep learning model. Investor anxiety is calculated by combining Naver's Corona search index and Corona confirmed information, analyzing Granger causality with cryptocurrency prices, and predicting cryptocurrency prices using deep learning models. The experimental results are as follows. First, CCI indicators showed significant Granger causality in the returns of Bitcoin, Ethereum, and Lightcoin. Second, LSTM with CCI as an input variable showed high predictive performance. Third, Bitcoin's price prediction performance was the highest in comparison between cryptocurrencies. This study is of academic significance in that it is the first attempt to analyze the relationship between Naver's Corona search information and cryptocurrency prices in the Corona phase. In future studies, extended studies into various deep learning models are needed to increase price prediction accuracy.

• Proceedings of the Korea Information Processing Society Conference
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• 2006.11a
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• pp.157-160
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• 2006

웹 자원이 방대함에 따라, 사용자가 원하는 정보를 얼마나 정확하게 제시하느냐가 정보검색시스템 성능을 판단하는 기준이 된다. 그러나 동형이의어만을 질의어로 이용한 검색 결과는 동형이의어 각 의미에 관련된 문서가 혼재되어 있거나, 특정 의미에 관련된 문서가 집중적으로 나타나는 현상을 볼 수 있다. 이에 본 논문에서는 한국어 사용자 어휘지능망(U-WIN)의 관계정보를 이용하여 질의어의 모호성을 해결하고 의미적 정보검색의 기반을 마련하고자 한다. 우선, 전문분야에 주로 사용되는 동형이의어와 보편적으로 사용하는 동형의어를 구번하여 질의어로 선정하고, '질의어+상위어' 형태의 확장 질의어에 대해 두 개의 포탈사이트(Google, Naver)를 대상으로 웹 문서를 검색하여 정확률이 각각 81.5%(Naver), 65.5%(Google)로 나타났다.