• The Journal of the Convergence on Culture Technology
• /
• v.8 no.4
• /
• pp.277-287
• /
• 2022

Recent trends in sharing online video contents show that vlogging on everyday life can cause meaningful changes in viewers' perception of mediated characters and their own lifestyles. We explored that flourishing everyday life video contents online are intertwined with viewers' psychological engagement such as empathy and social presence, let alone self-enhancing motivations such as self-reflection and psychological empowerment. Survey analysis results suggest that between contents types of vlog (everyday life vs. in-depth information), watching in-depth information videos significantly affected viewers' psychological engagement and self-enhancing motivations. Compared to celebrity's online videos, daily videos starring laypeople showed significantly positive impacts on viewers' engagement such as empathy, social presence, and self-enhancement such as self-reflection, psychological empowerment.

• Journal of Korean Society of Transportation
• /
• v.32 no.5
• /
• pp.462-472
• /
• 2014

As the Rope Type Platform Safety Door (RPSD) is now commercially available, the technology of RSPD and the public sentiment towards RPSD are being scrutinized. During the period of RPSD development and trial installation, there has been a need to examine its technical reliability and safety, and its users' emotional attitudes. Though often dichotomized in practice, technological innovation of, and the public sentiment towards RPSD are directly related to continuing and collaborated efforts to enhance public satisfaction with the service. Therefore, based on the analyses of public sentiment towards the RPSD system and the log files of operation, this study evaluates public satisfaction with RPSD during its trial phase at Munyang Station in the Daegu Subway System.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.32 no.1
• /
• pp.25-38
• /
• 2022

With the diversification of payment methods and games, related financial accidents are causing serious problems for users and game companies. Recently, game companies have introduced an Fraud Detection System (FDS) for game payment systems to prevent financial incident. However, FDS is ineffective and cannot provide major evidence based on judgment results, as it requires constant change of detection patterns. In this paper, we analyze abnormal transactions among payment log data of real game companies to generate related features. One of the unsupervised learning models, Autoencoder, was used to build a model to detect abnormal transactions, which resulted in over 85% accuracy. Using X-FDS (Explainable FDS) with XAI-SHAP, we could understand that the variables with the highest explanation for anomaly detection were the amount of transaction, transaction medium, and the age of users. Based on X-FDS, we derive an improved detection model with an accuracy of 94% was finally derived by fine-tuning the importance of features that adversely affect the proposed model.

• Proceedings of the Korean Information Science Society Conference
• /
• 2006.10c
• /
• pp.154-159
• /
• 2006

본 논문은 웹 서버에 의해 자동으로 수집되는 로그 파일로부터 고객 가치 판단 기준을 고객의 행동 기반에 두고 군집화 기법을 이용하여 고객을 세분화하고 세분화 결과에 의사결정나무를 적용함으로써 고객을 분류하는 통합 모형을 제안하였다. 또한, 분류된 고객들의 주 서비스 활용 패턴을 분석하기 위하여 연관규칙기법을 적용하여 고객의 과학기술정보 활용의 연관성을 분석함으로써, 과학정보포털 서비스를 제공하는 사이트 이용자의 분류군에 해당하는 정보와 인터페이스를 제공하는 새로운 방법에 대하여 연구하였다. 고객 관리 측면에서 본 논문은 정보 서비스를 제공하는 웹 사이트의 기존고객을 분류하여 패턴을 분석함으로써 고객 위주의 사이트 운영정책과 동적 인터페이스를 제공하기 위한 웹사이트 활용 방안을 제시하였다. 또한, 고객의 지속적인 관리라 각 고객 분류군별에 안는 서비스를 제공하고 고객의 관리에도 기여할 수 있을 것이다.

• Journal of Convergence for Information Technology
• /
• v.10 no.12
• /
• pp.9-14
• /
• 2020

Currently, NTIS (National Technology Information Service) is building an interactive search service based on artificial intelligence technology. In order to understand users' search intentions and provide R&D information, an interactive search service is built based on deep learning models and morpheme analyzers. The deep learning model learns based on the log data loaded when using NTIS and interactive search services and understands the user's search intention. And it provides task information through step-by-step search. Understanding the search intent makes exception handling easier, and step-by-step search makes it easier and faster to obtain the desired information than integrated search. For future research, it is necessary to expand the range of information provided to users.

• Journal of the Korean BIBLIA Society for library and Information Science
• /
• v.23 no.4
• /
• pp.283-308
• /
• 2012

With explosive growth of information by recent advancements in information technology and the Internet, users need a method to acquire appropriate information. To solve this problem, an information retrieval and filtering system was developed as an important tool for users. Also, users and service providers are growing more and more interested in personalized information recommendation. This study designed and implemented personalized information recommendation system based on AR as a method to provide positive information service for information users as a method to provide positive information service. To achieve the goal, the proposed method overcomes the weaknesses of existing systems, by providing a personalized recommendation method for contents that works in a large-scaled data and user environment. This study based on the proposed method to extract rules from log files showing users' behavior provides an effective framework to extract Association Rule.

• Journal of Internet Computing and Services
• /
• v.21 no.4
• /
• pp.87-95
• /
• 2020

The Science and Technology Information Service provided by the Korea Institute of Science and Technology Information (KISTI) is a service designed to allow users to easily and conveniently search and view content that is built similar to the general information service. NDSL is KISTI's core science, technology and information service, providing about 138 million content and having about 93 million page views in a year of 2019. In this paper, various insights were derived through the analysis of how science and technology information such as academic papers, reports and patents provided by NDSL is searched and utilized through web services (https://www.ndsl.kr) and search query words. In addition to general statistics such as the status of content construction, utilization status and utilization methods by type of content, monthly/weekly/time-of-day content usage, content view rate per one-time search by content type, the comparison of the use status of academic papers by year, the relationship between the utilization of domestic academic papers and the KCI index we analyzed the usability of each content type, such as academic papers and patents. We analyzed query words such as the language form of query words, the number of words of query words, and the relationship between query words and timeliness by content type. Based on the results of these analyses, we would like to propose ways to improve the service. We suggest that NDSL improvements include ways to dynamically reflect the results of content utilization behavior in the search results rankings, to extend query and to establish profile information through non-login user identification for targeted services.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.18 no.4
• /
• pp.1000-1006
• /
• 2014

This study focuses on the fact that a OTT service is vigorously used for smart phones, and suggests a design of method to trace the experiences of watching television contents. For this purpose, we developed logging functions and embedded them into existing OTT service application to record flow and pattern of watching context. This paper suggests a log file format which can accurately and precisely record watching actions of users per-second methodology rather than former per-minute methodology. Moreover, this study shows that the application can trace watching attitude according to occurring events by characteristics and playing modes of realtime broadcasting, VOD, advertisement contents. In addition, based on the result of the study, this paper discusses educational, operational meaning of the method such as methodological application in mobile ethnography field or survey for total screening rate.

• KIPS Transactions on Software and Data Engineering
• /
• v.10 no.6
• /
• pp.223-234
• /
• 2021

Recent advances in the 4th Industrial Revolution have accelerated the change of the shopping behavior from offline to online. Search queries show customers' information needs most intensively in online shopping. However, there are not many search query research in the field of search, and most of the prior research in the field of search query research has been studied on a limited topic and data-based basis based on researchers' qualitative judgment. To this end, this study defines the type of search query with data-based quantitative methodology by applying machine learning to search research query field to define the 15 topics of search query by conducting topic modeling based on search query and clicked document information. Furthermore, we present a new classification system of new search query types representing searching behavior characteristics by extracting key variables through principal component analysis and analyzing. The results of this study are expected to contribute to the establishment of effective search services and the development of search systems.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.22 no.2
• /
• pp.225-238
• /
• 2012

Among the various security threats in online games, the use of game bots is the most serious problem. In this paper, we propose a framework for user behavior analysis for bot detection in online games. Specifically, we focus on party play that reflects the social activities of gamers: In a Massively Multi-user Online Role Playing Game (MMORPG), party play log includes a distinguished information that can classify game users under normal-user and abnormal-user. That is because the bot users' main activities target on the acquisition of cyber assets. Through a statistical analysis of user behaviors in game activity logs, we establish the threshold levels of the activities that allow us to identify game bots. Also, we build a knowledge base of detection rules based on this statistical analysis. We apply these rule reasoner to the sixth most popular online game in the world. As a result, we can detect game bot users with a high accuracy rate of 95.92%.