• Journal of Smart Tourism
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• v.4 no.1
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• pp.19-29
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• 2024

The objective of this article is to create a model of integrated management from the framework modeling of a digital collaborative network supported by knowledge engineering to make heritage site in the Brazil more effective. It is an exploratory and qualitative research with thematic analysis as technique of data analysis from the collaborative network, digital platform, world heritage, and tourism themes. The snowballing approach was chosen, and the mapping and classification of relevant studies was developed with the use of the spreadsheet tool and the Mendeley® software. The results show that the collaborative network model oriented towards strategic objectives should be supported by a digital platform that provides a technological environment that adds functionalities and digital platform services with the integration of knowledge engineering techniques and tools, enabling the discovery and sharing of knowledge in the collaborative network.

• Journal of Digital Convergence
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• v.11 no.11
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• pp.693-704
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• 2013

The purpose of this study was to analyze the effects of social network service on the collaborative skills, collaborative satisfaction, and interaction within groups in collaborative learning. The group that used KakaoTalk, one of social network service for working on the collaborative task in the course exhibited higher collaborative skills and collaborative satisfaction (p<.05) than the group that did not use KakaoTalk. When analyzing the amount and the content of the messages produced by the group that used KakaoTalk, the amount of messages did not have an impact on the collaborative skills and collaborative satisfaction.

• Journal of Korea Society of Digital Industry and Information Management
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• v.9 no.1
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• pp.43-52
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• 2013

Pervasive computing and social network are good resources in recommendation method. Collaborative filtering is one of the most popular recommendation methods, but it has some limitations such as rating sparsity. Moreover, it does not consider social network in pervasive computing environment. We propose an effective proactive friend recommendation method using social network and contexts in pervasive computing environment. In collaborative filtering method, users need to rate sufficient number of items. However, many users don't rate items sufficiently, because the rating information must be manually input into system. We solve the rating sparsity problem in the collaboration filtering method by using contexts. Our method considers both a static and a dynamic friendship using contexts and social network. It makes more effective recommendation. This paper describes a new friend recommendation method and then presents a music friend scenario. Our work will help e-commerce recommendation system using collaborative filtering and friend recommendation applications in social network services.

• Journal of Korean Library and Information Science Society
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• v.36 no.4
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• pp.329-347
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• 2005

Collaborative Digital Reference Service( CDRS) has spread worldwide as a tool of obtaining high-quality information in knowledge information society KISTI formed CDRS collaboration model called Question포인트+ with 4 institutions in Korea. In Question포인트+ which is also linked to the Global Network, the domestic and oversea information specialists answer a variety of questions in response of the requests of users on the web. The implementation of Question포인트+ is significant as the first domestic CDRS collaboration model, and contribute to management, dissemination, and activation of CDRS in Korea.

• Journal of the Korean Society for Library and Information Science
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• v.45 no.1
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• pp.11-27
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• 2011

This paper aims to seek ways of optimizing the operations of Collaborative Digital Reference Service(CDRS) in Korea. CDRS tries to specialize the reference service through world-wide collaborative service. In this study seven international CDRS services including "QuestionPoint" operated by ALA and one national service, "Ask a Librarian" by the National Library of Korea were surveyed. A focused analysis of CDRS in Korea shows not only a sharp increase in use by the public, but also much application in research and academic activities by university students and workers.

• Journal of Information Science Theory and Practice
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• v.10 no.spc
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• pp.12-24
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• 2022

This study attempted to investigate changes in collaboration structure for each stage of national Research and Development (R&D) in the artificial intelligence (AI) field through analysis of a co-author network for papers written under national R&D projects. For this, author information was extracted from national R&D outcomes in AI from 2014 to 2019. For such R&D outcomes, NTIS (National Science & Technology Information Service) information from the KISTI (Korea Institute of Science and Technology Information) was utilized. In research collaboration in AI, power function structure, in which research efforts are led by some influential researchers, is found. In other words, less than 30 percent is linked to the largest cluster, and a segmented network pattern in which small groups are primarily developed is observed. This means a large research group with high connectivity and a small group are connected with each other, and a sporadic link is found. However, the largest cluster grew larger and denser over time, which means that as research became more intensified, new researchers joined a mainstream network, expanding a scope of collaboration. Such research intensification has expanded the scale of a collaborative researcher group and increased the number of large studies. Instead of maintaining conventional collaborative relationships, in addition, the number of new researchers has risen, forming new relationships over time.

• Journal of Digital Convergence
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• v.10 no.6
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• pp.33-39
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• 2012

The purpose of this study is to investigate the effect of SNS(Social Network Service) on individual learning performance. To do this, we distribute and collect data by using a survey method. Research results suggest that online social networking engagement and acculturation have an effect on interaction quality with professors. Interaction quality with professors influences individual learning performance as well as collaborative learning. The conclusion and implications are discussed.

• Journal of Korea Multimedia Society
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• v.23 no.2
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• pp.343-350
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• 2020

In the era of the Fourth Industrial Revolution in the digital revolution is taking place, various attempts have been made to provide various contents in a digital environment. In this paper, agent-recruitment service system based on collaborative deep learning is proposed for the intelligent head hunting service. The service system is improved from previous research [7] using collaborative deep learning for more reliable recommendation results. The Collaborative deep learning is a hybrid recommendation algorithm using "Recurrent Neural Network(RNN)" specialized for exponential calculation, "collaborative filtering" which is traditional recommendation filtering methods, and "KNN-Clustering" for similar user analysis. The proposed service system can expect more reliable recommendation results than previous research and showed high satisfaction in user survey for verification.

• Proceedings of the Korea Contents Association Conference
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• 2007.11a
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• pp.365-368
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• 2007

Information institutes in the world provide users with collaborative digital reference service for answering users' questions. Korea Institute of Science and Technology Information has conducted Question포인트+ that is CDRS using global mechanism. KISTI has developed two types of global collaboration mechanism on CDRS: one is using Global Network, the other one is direct cooperation between KISTI and a German organization ZLB. This study analyzes the current global collaboration mechanism of CDRS at KISTI and suggests the future model of global collaboration mechanism to satisfy the users' needs.

• Journal of Digital Convergence
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• v.17 no.1
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• pp.123-130
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• 2019

One of the most commonly used methods of web recommendation techniques is collaborative filtering. Many studies on collaborative filtering have suggested ways to improve accuracy. This study proposes a method of movie recommendation using Word2Vec and an ensemble convolutional neural networks. First, in the user, movie, and rating information, construct the user sentences and movie sentences. It inputs user sentences and movie sentences into Word2Vec to obtain user vectors and movie vectors. User vectors are entered into user convolution model and movie vectors are input to movie convolution model. The user and the movie convolution models are linked to a fully connected neural network model. Finally, the output layer of the fully connected neural network outputs forecasts of user movie ratings. Experimentation results showed that the accuracy of the technique proposed in this study accuracy of conventional collaborative filtering techniques was improved compared to those of conventional collaborative filtering technique and the technique using Word2Vec and deep neural networks proposed in a similar study.