• Journal of Digital Convergence
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• v.14 no.5
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• pp.309-316
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• 2016

While consumption patterns have changed online music, online music platform began to emerge. While people prefer popular music recommendation, they use the online music platform chart or use the SNS Platform to share information. Online platform Ranking is different because of different properties held by members. Meanwhile, we need music charts characteristics of SNS users. So there were a lot of attempts to chart a comprehensive variety of platforms. And continue to emerge theses linking the musical characteristics and SNS. In this paper, We have developed a new chart using the behavior of Twitter Users who listen to music, and studies comparing the results with existing chart.

• Journal of the Korea Convergence Society
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• v.9 no.8
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• pp.25-32
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• 2018

Recently, the size and type of disasters in Korea has been diversified. However, Korea has not been able to build various information support systems to predict these disasters.Many other organizations also provide relevant information. This information is mainly provided on the Web, but most of it is not real time information. In this study, we have paid attention to support information using big data to provide better quality real - time information together with information provided by institutions. Big data has a large amount of information with real-time property, and it can make customized service using it. Among them, SNS such as Twitter and Facebook can be used as a new information collection medium in case of disaster. However, it is very difficult to retrieve necessary information from too much information, and it is difficult to collect intuitive information. For this purpose, this study develops an information support system using Twitter. The system retrieves information using the Twitter hashtag. Also, information mapping is performed on the map so that intuitive information can be grasped. For system evaluation, information extraction, degree of mapping, and recommendation speed are evaluated.

• Journal of Broadcast Engineering
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• v.20 no.6
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• pp.848-861
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• 2015

In this paper, we introduce a new web-based PR video making service system. Many video editing tools have required tough editing skill or scenario planning stage for a just simple PR video making. Some users may prefer a simple and fast way than sophisticated and complex functionality. To solve this problem, it is important to provide easy user interface and intelligent classification and recommendation scheme. Therefore, we propose a new template classification and recommendation scheme using a topic modeling method. The proposed scheme has the big advantage of being able to handle the unstructured meta data as well as structured one.

• Smart Media Journal
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• v.9 no.4
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• pp.118-125
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• 2020

This paper is conducted for automatically recommending and providing information services desired by users on websites of local governments and public institutions with vast amounts of information, In this system, we defined a method of collecting data based on the SiiRU CMS system that collects and preprocesses data, and a study that provides curation services (contents and menus) to users through a collaborative filtering algorithm based on machine learning. Also, the data used in the paper is conducted based on about 1 million data collected in 2019. The analyzed data can provide important information that cannot be easily accessed by providing a cloud tag service or recommended menu for users to conveniently view, and the environment configuration that can realize this service to local governments and public institutions is also provided.

• 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.

• Journal of Korean Library and Information Science Society
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• v.45 no.1
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• pp.339-358
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• 2014

This study aims to construct faceted navigation useful for domestic users through analyzing English and North American public libraries' faceted navigations. The 19 domestic libraries and the 12 foreign libraries has been chosen for analyzing their unique facets. Although the domestic libraries' facets had no differences to the domestic universities' facets, the North American libraries have their unique facets. Those facets are target audience, genre, library list, user tags, and availability related facets and the facets are mainly to improve user's usability in using library catalog. Adopting the facets in domestic public libraries by using Fixed-Length data elements of KORMARC, domestic public libraries will enhance usability of library users in retrieving library materials.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.2
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• pp.157-162
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• 2008

This paper presents a learning method of tagging ontology using large tagging data such as a folksonomy, which stands for classification structure informally created by the people. There is no common agreement about the semantics of a tagging, and most social web sites internally use different methods to represent tagging information, obstructing interoperability between sites and the automated processing by software agents. To solve this problem, we need a tagging ontology, defined by analyzing intrinsic attributes of a tagging. Through several machine learning for tagging data, tag groups and similar user groups are extracted, and then used to learn the tagging ontology. A recommender system adopting the tagging ontology is also suggested as an applying field.

• Proceedings of the Korean Society of Computer Information Conference
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• 2014.01a
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• pp.127-130
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• 2014

여행지에서 제공하고 있는 스탬프 투어링 서비스는 여행객들에게 특별한 기념이 되어주고 있지만, 스탬프를 받기 위한 장소를 찾기 어려운 점과 여행지의 주변 정보를 얻기 위해서는 여행자의 별도의 노력이 필요하다는 단점이 있다. 이러한 문제를 해결하기 위해 유명 관광지 및 추천 명소 등에 NFC 태그를 설치하고 간단한 태깅만으로 스탬프를 수집할 수 있는 어플리케이션을 제안한다. 또한 이 어플리케이션에 저장되는 정보와 평가를 이용해 사용자별 맞춤 여행 코스를 제공하고 사용자가 좀 더 편리하고 재미있는 여행을 즐길 수 있도록 돕고자 한다.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.1
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• pp.82-88
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• 2017

Recently recommendation methods need personalization and diversity as well as accuracy whereas the traditional researches have been mainly focused on the accuracy of recommendation in terms of quality. The diversity of recommendation is also important to people in terms of quantity in addition to quality since people's desire for content consumption have been stronger rapidly than past. In this paper, we pay attention to similarity of data gathered simultaneously among different types of contents. With this motivation, we propose an enhanced recommendation method using correlation analysis with considering data similarity between two types of contents which are movie and music. Specifically, we regard folksonomy tags for music as correlated data of genres for movie even though they are different attributes depend on their contents. That is, we make result of new recommendation movie items through mapping music folksonomy tags to movie genres in addition to the recommendation items from the typical collaborative filtering. We evaluate effectiveness of our method by experiments with real data set. As the result of experimentation, we found that the diversity of recommendation could be extended by considering data similarity between music contents and movie contents.

• Journal of the Korean Society for Library and Information Science
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• v.52 no.3
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• pp.5-33
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• 2018

In this paper, we analyze a user's usage behavior, identify and track search intention and interest field based on the National Science and Technology Standard Classification, and use it to personalize science and technology information. In other words, we sought to satisfy both efficiency and satisfaction in searching for information that users want by improving scientific information search performance. We developed the personalization service of science and technology information and evaluated the suitability and usefulness of personalized information by comparing the search performance between expert experimental group and control group. As a result, the personalization service proposed in this study showed better search performance than comparative service and proved to provide higher usability.