• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.1
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• pp.17-25
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• 2018

Recently, with the development of ICT technology and the rapid spread of smart devices, a huge amount of information is being generated. The recommendation system has helped the informant to judge the information from the information overload, and it has become a solution for the information provider to increase the profit of the company and the publicity effect of the company. Recommendation systems can be implemented in various approaches, but social information is presented as a way to improve performance. However, no research has been done to utilize trust cluster information among users in the recommendation system. In this paper, we propose a method to improve the performance of the recommendation system by using the influence between the intra-cluster objects and the information between the trustor-trustee in the cluster generated in the online review. Experiments using the proposed method and real data have confirmed that the prediction accuracy is improved than the existing methods.

• Proceedings of the Korea Information Processing Society Conference
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• 2010.04a
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• pp.1066-1069
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• 2010

본 연구는 최근 블로그 추천 연구의 주요 쟁점으로 제기되는 추천 후보의 선정과 추천 후보 평가에 접근한다. 첫 번째로 추천 후보 선정은 추천 요구자와 소셜 네트워크 관계에 있는 블로그를 중심으로 진행한다. 이러한 접근방식은 추천 요구자가 타 블로그와 직접적인 관계를 많이 이루지 못했을 경우 다수의 간접 연결 블로그가 추천 후보로 차지하게 된다. 직접 관계의 희소함으로 인하여 추천 후보와 추천 요구자와의 연관성이 전체적으로 저하되는 문제에 초점을 맞추어 추천 대상을 내용 기반의 클러스터 단위로 선정하는 방식을 제안한다. 또한 추천 대상 블로그의 평가에서는 소셜 네트워크 및 내용 평가를 결합시킴으로써 요구자에게 보다 적합한 추천 결과를 제시한다.

• Journal of KIISE
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• v.45 no.2
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• pp.126-133
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• 2018

The recommender system (RS) has emerged as a solution to overcome the constraints of excessive information provision and to maximize profit and reputation for information providers. Although the RS can be implemented with various approaches, there is no study on how to appropriately utilize the information generated from the review of the recommended object. We propose a method to improve the performance of RS by using cluster information generated from online review. We implemented the proposed method and experimented with real data, and confirmed that the performance is significantly improved compared to the existing approaches.

• KIPS Transactions on Software and Data Engineering
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• v.5 no.11
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• pp.521-526
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• 2016

As on-line informal text data have massive in its volume and have unstructured characteristics in nature, there are limitations in applying traditional relational data model technologies for data storage and data analysis jobs. Moreover, using dynamically generating massive social data, social user's real-time reaction analysis tasks is hard to accomplish. In the paper, to capture easily the semantics of massive and informal on-line documents with unsupervised learning mechanism, we design and implement automatic topic extraction systems according to the mass of the words that consists a document. The input data set to the proposed system are generated first, using N-gram algorithm to build multiple words to capture the meaning of the sentences precisely, and Hadoop and Spark (In-memory distributed computing framework) are adopted to run topic model. In the experiment phases, TB level input data are processed for data preprocessing and proposed topic extraction steps are applied. We conclude that the proposed system shows good performance in extracting meaningful topics in time as the intermediate results come from main memories directly instead of an HDD reading.

• Information Systems Review
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• v.19 no.2
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• pp.21-35
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• 2017

The advancement of IT and the "Fourth Industrial Revolution" blurred the boundary between industries. The importance of strategic cooperation between enterprises is emphasized. IT companies must consider their existing business areas and create new territories to drive changes in the industry. They must also secure their competitive edge and manage economic costs to enable them to compete with their global counterparts. By utilizing their resources effectively, these firms can create value through inter-firm cooperation. This study analyzes the collaborative network of global IT companies using social network analysis and examines the effect of this network on firm performance. Collaborative linkages and betweenness centrality, which represent the bridging position of a firm in a network, significantly affect firm performance. This result highlights the importance of the structural position of a firm in a cooperative network of IT companies. This study also characterizes clusters in a network of IT companies. Most of these clusters comprise a combination of IT companies in diverse IT industries. These clusters suggest that these companies engage in multilateral cooperation without boundaries to maximize their business capabilities. This study offers practical implications for establishing a cooperative strategy and framework that can capture business trends in the IT industry from a macroscopic view. This study also visualizes collaborative networks in a multifaceted way using social network analysis to provide researchers and business practitioners with an informative viewpoint.

• Journal of Korea Entertainment Industry Association
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• v.13 no.4
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• pp.173-184
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• 2019

The data processing of this study is based on the word data search in social media through textom and the big data analysis is carried out and three areas (2019 Gwangju World Swimming Championships, 2019 Gwangju World Swimming Masters Competition, 2019 World Swimming Championships Problem) was consistently handled through data collection and refinement in the web environment. We applied the collected words to the program of Ucinet6, visualized them, and conducted a CONCOR analysis to grasp the similar relationship of words and to identify the cluster of common factors. As a result of the analysis, the clusters related to the 2019 Gwangju World Swimming Championships mainly consisted of four major areas of recognition and perception, mainly searching for operational aspects related to the swimming championship, and the community related to the 2019 Gwangju World Swimming Masters Competition Is mainly searched for the promotion of the Masters Competition and the aspect of the competition divided into two areas of major recognition and peripheral recognition. The cluster related to the problems of the 2019 Gwangju World Swimming Championships is divided into five areas, And they are mainly searching for the place, operation, institution, event, etc. of the problem of the swimming championship.

• The Journal of the Convergence on Culture Technology
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• v.10 no.3
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• pp.97-109
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• 2024

In the field of domestic tourism, tourism trend analysis of tourism consumers, both international tourists and domestic tourists, is essential not only for the Korean tourism market but also for local and governmental tourism policy makers. e will explore the keywords and sentiment analysis on social media to establish a marketing strategy plan and revitalize the domestic tourism industry through communication and information from tourism consumers. This study utilized TEXTOM 6.0 to analyze recent trends in Korean tourism. Data was collected from September 31, 2022, to August 31, 2023, using 'Korean tourism' and 'domestic tourism' as keywords, targeting blogs, cafes, and news provided by Naver, Daum, and Google. Through text mining, 100 key words and TF-IDF were extracted in order of frequency, and then CONCOR analysis and sentiment analysis were conducted. For Korean tourism keywords, words related to tourist destinations, travel companions and behaviors, tourism motivations and experiences, accommodation types, tourist information, and emotional connections ranked high. The results of the CONCOR analysis were categorized into five clusters related to tourist destinations, tourist information, tourist activities/experiences, tourism motivation/content, and inbound related. Finally, the sentiment analysis showed a high level of positive documents and vocabulary. This study analyzes the rapidly changing trends of Korean tourism through text mining on Korean tourism and is expected to provide meaningful data to promote domestic tourism not only for Koreans but also for foreigners visiting Korea.