• The Journal of the Korea Contents Association
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• v.17 no.2
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• pp.404-414
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• 2017

With the development of smartphones and online social networks, users produce a lot of contents and share them with each other. Therefore, users spend time by viewing or receiving the contents they do not want. In order to solve such problems, schemes for recommending useful contents have been actively studied. In this paper, we propose a contents recommendation scheme using collaborative filtering for users on online social networks. The proposed scheme consider a user trust in order to remove user data that lower the accuracy of recommendation. The user trust is derived by analyzing the user activity of online social network. For evaluating the user trust from various points of view, we collect user activities that have not been used in conventional techniques. It is shown through performance evaluation that the proposed scheme outperforms the existing scheme.

• The Journal of the Korea Contents Association
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• v.19 no.3
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• pp.171-182
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• 2019

As online shopping malls continue to grow in popularity, various chances of consumption are provided to customers. Customers decide the purchase by exploiting information provided by shopping malls such as the reviews of actual purchasing users, the detailed information of items, and so on. It is required to provide objective and reliable information because customers have to decide on their own whether the massive information is credible. In this paper, we propose a personalized recommendation method considering an item confidence to recommend reliable items. The proposed method determines user preferences based on various behaviors for personalized recommendation. We also propose an user preference measurement that considers time weights to apply the latest propensity to consume. Finally, we predict the preference score of items that have not been used or purchased before, and we recommend items that have highest scores in terms of both the predicted preference score and the item confidence score.

• Journal of the Korea Computer Graphics Society
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• v.14 no.4
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• pp.35-39
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• 2008

A computer supported collaborative system provides with a shared virtual workspace over the Internet where its remote users cooperate in order to achieve their goals by overcoming problems caused by distance and time. VRMMS (Virtual Reality Molecular Modeling System) [1] is a VR based collaborative system where biologists can remotely participate in and exercise molecular modeling tasks such as viewing three dimensional structures of molecular models, confirming results of molecular simulations and providing with feedbacks for the next simulations. Biologists can utilize VRMMS in executing molecular simulations. However, first-time users and beginners need to spend some time for studying and practicing in order to skillfully manipulate molecular models and the system. The best way to resolve the problem is to have a face-to-face session of teaching and learning VRMMS. However, it is not practically recommended in the sense that the users are remotely located. It follows that the learning time could last longer than desired. In this paper, we propose to use Second Life [2] combining with VRMMS for removing the problem. It can be used in building a shared workplace over the Internet where molecular simulations using VRMMS can be exercised, taught, learned and practiced. Through the web, users can collaborate with each other using VRMMS. Their avatars and tools of molecular simulations can be remotely utilized in order to provide with senses of 'being there' to the remote users. The users can discuss, teach and learn over the Internet. The shared workspaces for discussion and education are designed and implemented in Second Life. Since the activities in Second Life and VRMMS are designed to realistic, the system is expected to help users in improving their learning and experimental performances.

• Journal of Korea Game Society
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• v.15 no.5
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• pp.153-162
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• 2015

This study explores the social sides of games by investigating the relationship between cooperative games and psychological factors determining social relationships (i.e., self-disclosure, intimacy) and how such psycosocial factors influence offline social participations. Utilizing both online and offline surveys, the results indicated that the amount of time spending on cooperative games have a positive impact on self-disclosure and intimacy. The results also showed that users' self-disclosure and intimacy influence offline social participation.

• Journal of KIISE
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• v.44 no.7
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• pp.726-732
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• 2017

In practical distributed sensing and prediction applications over wireless sensor networks (WSN), environmental sensing activities are highly dynamic because of noisy sensory information from moving source signals. The recent distributed online convex optimization frameworks have been developed as promising approaches for solving approximately stochastic learning problems over network of sensors in a distributed manner. Negligence of mobility consequence in the original distributed saddle point algorithm (DSPA) could strongly affect the convergence rate and stability of learning results. In this paper, we propose an integrated sliding windows mechanism in order to stabilize predictions and achieve better convergence rates in cooperative detection of a moving source signal scenario.

• Proceedings of the Korea Information Processing Society Conference
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• 2019.10a
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• pp.781-784
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• 2019

최근 AI를 산업 서비스에 적용하기 위해 많은 회사들이 활발히 연구를 하고 있다. 아마존과 넷플릭스 같은 거대 기업들은 이미 빅데이터와 AI 머신러닝을 이용한 추천 시스템을 구현하였고 아마존은 매출의 35%가 추천에 의해 발생하고 넷플릭스 75%의 사용자가 추천을 통해 영화를 선택한다고 보고되었다. 이러한 두 기업의 높은 추천 효율성의 이유는 협업 필터링(Collaborative filtering)과 같은 다양한 추천 알고리즘과 방대한 상품 및 고객 행동(구매, 시청 등) 데이터 등이 존재하고 있기 때문이다. 기계학습에서 알고리즘 학습을 위한 데이터의 양이 많지 않을 경우 알고리즘의 성능을 보장할 수 없다는 것이 일반적인 의견이다. 방대한 데이터를 가진 기업에서 추천 알고리즘을 적극적으로 활용 및 연구하고 있는 것도 이러한 이유 때문이다. 반면, 오프라인 및 여행사 기반에서 온라인 기반으로 영역을 차츰 확대하고 있는 항공 서비스 고객 데이터의 경우, 산업의 특성상 많은 회원에 비해 고객 1명당 온라인에서 활동하는 이력이 많지 않은 것이 특징이다. 이는, 추천 알고리즘을 통한 서비스 제공에서 큰 제약사항으로 작용한다. 본 연구에서는, 이러한 희박한 고객 활동 데이터에서 최신성 기반의 추천 시스템을 통하여 제약사항을 극복하고 추천 효율을 높이는 방법을 제안한다. 고객의 최근 접속 이력 로그를 시간 기준으로 데이터 셋을 분할하여 추천 알고리즘에 반영하였을 때, 추천된 노선에 대한 고객의 반응을 추천 성능 지표인 CTR(Click-Through Rate)로 측정하여 성능을 확인해 보았다.

• The Journal of the Korea Contents Association
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• v.11 no.12
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• pp.950-957
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• 2011

With the convergence of disciplines in progress and Web 2.0 online collaborative environment, online knowledge exchange activities of researchers are increasing. Quickness of acquiring knowledge highly impacts on research productivity in the global era. Online knowledge exchange is critical service for researchers. In this study, knowledge exchange service model was presented from the perspective of activate participation, knowledge quality improvement, quickness of exchanges. A variety of domestic and international knowledge exchange services were analyzed, particularly Korean domestic service KOSEN What is? as for operational practice. It is confirmed that in order to stimulate researcher knowledge exchange the quality of the knowledges exchanged is essential and variety of operating activities are needed such as expert matching systems, enhancement of speed in knowledge exchange, ease of usability, and elements of fun.

• Journal of Korea Multimedia Society
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• v.7 no.3
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• pp.377-387
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• 2004

This paper describes a tree-based conversational interface supporting efficient presentation of turn relations on online conversation. Most of conventional conversational interfaces are difficult to make use of formal conversation such as group meeting, decision-making, etc. due to very simplicity of a con versational interface and restriction of data structure of conversational messages. And a tree-based conversational interface supports formal conversation, but they are difficult to present turn relations because of jumpy display by locations of replied turns and distance between replied turns, etc. So this paper suggests a tree-based conversational interface to present efficiently turn relations using XML-based messages with merits of a text-based interface. The suggested conversational interface was implemented by using XML-, DOM, and JDK. And this paper showed that the conversational interface could be applied to conversation system using client- server architecture. Applications for the conversational interface are as follows: collaboration, distance teaming, online game, etc.

• Smart Media Journal
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• v.11 no.1
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• pp.9-16
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• 2022

With the development of online commercializing technology and the growth of online market, it has become extremely important for companies that aim to commercialize their products and services to control time and resources. Even when a single company has control over the entire process, it is necessary to maintain the efficiency among the process of development. In particular, when there is a lot of cooperation throughout the course of development, requiring many parties to communicate with each other, the issue of declining efficiency becomes much clearer. The timing of when to launch a product or service has become just as important as completing and testing them for companies. Companies have developed new tools to prevent situations that may lead to inefficiency in the development process, including an increase in the amount of resource used or issues with security maintenance. However, there is a lack of proper measurement tool that assesses what kind of additional benefits the entire process of the company is bringing, or whether or not the processes need to be improved in certain areas. Thus, this study aims to suggest a method to measure efficiency, to provide an empirical efficiency measurement method for the development process of online content.

• Journal of Intelligence and Information Systems
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• v.28 no.2
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• pp.1-18
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• 2022

As digital technology converges into the e-commerce market across industries, online transactions have activated, and the use of online has increased. With the recent spread of infectious diseases such as COVID-19, this market flow is accelerating, and various product information can be provided to customers online. Providing a variety of information provides customers with various opportunities but causes difficulties in decision-making. The recommendation system can help customers to make a decision more effectively. However, the previous research on recommendation systems is limited to only quantitative data and does not reflect detailed factors of products and customers. In this study, we propose an intelligent recommendation system that quantifies the attributes of products and customers by applying text mining techniques to qualitative data based on online reviews and integrates the existing objective indicators of total star rating, sentiment, and emotion. The proposed integrated recommendation model showed superior performance to the overall rating-oriented recommendation model. It expects the new business value to be created through the recommendation result reflecting detailed factors of products and customers.