• Journal of KIISE
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• v.43 no.11
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• pp.1188-1200
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• 2016

Recommendation systems play a significant role in providing personalized information to users, with enhanced satisfaction and reduced information overload. Since the mid-1990s, many studies have been conducted on recommendation systems, but few have examined the recommendations of information from people in the online social networking environment. In this paper, we present a hybrid recommendation method that combines both the traditional system of content-based techniques to improve specialization, and the recently developed system of social network-based techniques to best overcome a few limitations of the traditional techniques, such as the cold-start problem. By suggesting a state-of-the-art method, this research will help users in online social networks view more personalized information with less effort than before.

• Journal of the Korean Society for information Management
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• v.23 no.3 s.61
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• pp.91-125
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• 2006

Recent advancements in information technology and the Internet have caused an explosive increase in the information available and the means to distribute it. However, such information overflow has made the efficient and accurate search of information a difficulty for most users. To solve this problem, an information retrieval and filtering system was developed as an important tool for users. Libraries and information centers have been in the forefront to provide customized services to satisfy the user's information needs under the changing information environment of today. The aim of this study is to propose an efficient information service for libraries and information centers to provide a personalized recommendation system to the user. The proposed method overcomes the weaknesses of existing systems, by providing a personalized hybrid recommendation method for multimedia contents that works in a large-scaled data and user environment. The system based on the proposed hybrid method uses an effective framework to combine Association Rule with Collaborative Filtering Method.

• Proceedings of the Korea Contents Association Conference
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• 2009.05a
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• pp.1055-1060
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• 2009

In this paper, we design an e-Learning system for the Driver's License Teste studying through the Internet. The proposed system make users to be arrived at the goal for the license in a shorter time by offering learning contents and items according to the item-responses made by the users based on the Item Response Theory. Moreover we design the scheme to give the optimum items and the most necessary content to the user during the learning procedure in the form of concept-based objects. All the items in the problem bank DB maintain their difficulties, discriminations, and guessing parameters as is the case of 3-parameter logistic model. In addition user profile DB stores users' status informations, item responses, and ability parameters. Using these structures and combining agents, we can offer the optimum learning process or dynamic personalized studying structure to the user. We can construct interface agent and content selection and feedback agent with the DB's described above. User can study without any awareness of system operations or personal fitting scheme.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.47 no.4
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• pp.18-24
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• 2010

Mobile devices wide spread among users after the release of Apple's iPhone, especially in Korea. Mobile device has their own advantages in terms of weight, size, mobility and so on. But, on the contrary, mobile device has to provide more accurate and personalized information because of a small screen and a limited function of information retrieval. This paper presents a user"s preference element changeable recommender system by employing extended collaborative filtering as a technique to provide useful information in a mobile environment. Proposed system reflects user's similar groups by simultaneously considering users' information with preferences and demographic characteristics. Then we construct list of recommenders by user's choice. Finally, we show the implementation of a prototype based on iPhone.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.6
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• pp.50-59
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• 2014

In mobile healthcare service, the accurate detection and the notification of the emergency situation are important to chronic patients' life. In the existing healthcare service, the medical staff or medical service provider always judges patients' health status by monitoring from the measured from bio-data. However, it is difficult to monitor many patients in real-time simultaneously, because the medical staff should monitor the health status continuously. Furthermore, an emergency condition diagnosis based solely on the statistical level of the bio-data may be difficult, since the emergency judgment of the bio-data might differ depending on the health characteristics of each person such as age, history of disease, gender, etc. In order to solve this problem, this article presents an mobile healthcare system for emergency bio-data management using a personalized emergency policy. The salient feature of the proposed mobile healthcare system is that the characteristics of the health status of an unique patient is defined to the policy, which is used to judge the emergency condition of the bio-data measured from the patient. The prototype of proposed mobile healthcare system has been built to demonstrate the design concept.

• Journal of KIISE:Computer Systems and Theory
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• v.32 no.5
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• pp.209-218
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• 2005

In this paper, we propose CIVE, context-based interactive system for heterogeneous distributed virtual environments that delivers contexts irom real world to virtual environment and vice versa. The proposed system ronsists of obi-UCAM for generating user's contexts, NAVER for managing virtual environment, and Interface for linking obi-UCAM with NAVER. The connection between real and virtual worid through context is brneficial in following ways. Firstly. CIVE Provides a personalized user interface for virtual environment according to a user profile such as identity, age and vernacular. Secondly, translating all input signals into context, it guarantees adaptive access that enables a user to exploit unencumbered input devices controlling a shared object in virtual environment even if he moves with his own device from a virtual system to another. Finally, it provides a mechanism for synchronizing distributed virtual systems that share context representing changes at remote nodes. The context reduces the inconsistency of representing the same data among heterogeneous systems. Therefore, CIVE plays an important role in implementing VR applications such as teleconference, game and entertainment.

• Smart Media Journal
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• v.7 no.2
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• pp.47-53
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• 2018

Along with the development of information and communication technology, smart education that can learn without restrictions of time, place and equipment is activated even in the field of education. Although smart education is provided with content-based training solutions, construction of a system that grasps individual characteristics of learners and provides personalized learning is relatively weak. The activity of free choice is an important play activity of early childhood education, but it is not implemented efficiently by relying on the clinical observation of the teacher. If the IoT(Internet of Things) technology based on Hyper-Connected is applied to free-choice activities, it is possible to provide the child's personalized activity type and play-form analysis based on objective and stylized data. In this paper, we design and implement a system to monitor the child's activity of free choice by building an IoT environment that is based on open source hardware. The proposed system provides children's activity information as objective data and will be used as teacher's work mitigation and custom training material for each child.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.9
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• pp.1293-1304
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• 2022

The personalization recommendation system means analyzing each individual's interests or preferences and recommending information or products accordingly. These personalized recommendations can reduce the time consumers spend searching for information by accessing the products they need more quickly, and companies can increase corporate profits by recommending appropriate products that meet their needs. In this study, products are recommended to consumers using collaborative filtering, matrix factorization, and deep learning, which are representative personalization recommendation techniques. To this end, the data set after purchasing shopping mall products, which is raw data, is pre-processed in the form of transmitting the data set to the input of the recommended system, and the pre-processed data set is analyzed from various angles. In addition, each model performs verification and performance comparison on the recommended results, and explores the model with optimal performance, suggesting which model should be used when building the recommendation system at the mall.

• Journal of the Korea Society of Computer and Information
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• v.27 no.9
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• pp.59-68
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• 2022

A recommender system covers users, searches the items or services which users will like, and let users purchase them. Because recommendations from a recommender system are predictions of users' preferences for the items which they do not purchase yet, it is rarely possible to be drawn a perfect answer. An evaluation has been conducted to determine whether a prediction is right or not. However, it can be lower user's satisfaction if a recommender system focuses on only the preferences, that is caused by a 'filter bubble effect'. The filter bubble effect is an algorithmic bias that skews or limits the information an individual user sees on the recommended list. It is the reason why multiple metrics are required to evaluate recommender systems, and a diversity metrics is mainly used for it. In this paper, we compare three different methods for enhancing diversity for personalized recommendation - bin packing, weighted random choice, greedy re-ranking - with a practical e-commerce data acquired from a fashion shopping mall. Besides, we present the difference between experimental results and F1 scores.

• The Journal of the Convergence on Culture Technology
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• v.9 no.1
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• pp.347-353
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• 2023

Currently, soldiers enlisted in the military (Army) are receiving measurements (automatic, manual) of body parts and trying on sample clothing at boot training centers, and then receiving clothing in the desired size. Due to the low accuracy of the measured size during the measurement process, in the military, which uses a relatively more detailed sizing system than civilian casual clothes, the supplied clothes do not fit properly, so the frequency of changing the clothes is very frequent. In addition, there is a problem in that inventory is managed inefficiently by applying the measurement system based on the old generation body shape data collected more than a decade ago without reflecting the western-changed body type change of the MZ generation. That is, military uniforms of the necessary size are insufficient, and many unnecessary-sized military uniforms are in stock. Therefore, in order to reduce the frequency of clothing replacement and improve the efficiency of stock management, deep learning-based automatic measurement of body size, big data analysis, and machine learning-based "Personalized Combat Uniform Automatic Recommendation System for Enlisted Soldiers" is proposed.