• CRM연구
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• v.4 no.1
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• pp.55-68
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• 2011

Recently with the development of Web services by utilizing a variety of web content, the studies on user experience and personalization based on web usage has attracted much attention. Majority of personalized analysis are have been carried out based on existing data, primarily using the database and statistical models. These approaches are difficult to reflect in a timely mannerm, and are limited to reflect the true behavioral characteristics because the data itself was just a result of customers' behaviors. However, recent studies and commercial products on web analytics try to track and analyze all of the actions from landing to exit to provide personalized service. In this study, by analyzing the customer's click-stream behaviors, we define U-Score(Usage Score), P-Score (Preference Score), M-Score(Mania Score) to indicate variety of customer preferences. With the devised three indicators, we can identify the customer's preferences more precisely, provide in-depth customer reports and customer relationship management, and utilize personalized recommender services.

• Journal of KIISE:Software and Applications
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• v.35 no.8
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• pp.522-528
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• 2008

Topic map typically provide information to user through the selection of topics, that is using only topic, association, occurrence on the first topicmap which is made by domain expert without regard to individual interests or context, for the purpose of supplementation for the weakness which is providing personalized topic map information, personalization has been studied for supporting user preference through preseting of customize, filtering, scope, etc in topic map. Nevertheless, personalization in current topicmap is not enough to user so far. In this paper, we propose a design of PTRS(personalized topicmap ranking system) & algorithm, using both user profile(click through data) and basic element of topic map(topic, association) on knowledge layer in specific domain topicmap, therefore User has strong point that is improvement of personal facilities to user through representation of ranked topicmap information in consideration of user preference using PTRS.

• Journal of Broadcast Engineering
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• v.18 no.1
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• pp.88-97
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• 2013

Collaborative filtering(CF) for the personalized recommendation is a successful and popular method in recommender systems. But the mainly researched and implemented cases focus on dealing with independent items with explicit feedback by users. For the domain of TV program recommendation in VOD service platform, we need to consider the unique characteristic and constraints of the domain. In this paper, we studied on the way to convert the viewing history of each TV program episodes to the TV program preference by considering the series structure of TV program. The former is implicit for personalized preference, but the latter tells quite explicitly about the persistent preference. Collaborative filtering is done by the unit of series while data gathering and final recommendation is done by the unit of episodes. As a result, we modified CF to make it more suitable for the domain of TV program VOD recommendation. Our experimental study shows that it is more precise in performance, yet more compact in calculation compared to the plain CF approaches. It can be combined with other existing CF techniques as an algorithm module.

• The Journal of Korean Association of Computer Education
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• v.14 no.1
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• pp.91-109
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• 2011

This study describes the design and development of personalized e-Tutor to facilitate e-Learning. An e-Tutor was developed to provide personalized cognitive, emotional, and social learning supports automatically and integrated into an e-learning system. Fourteen learning supports selected in this study were consisted of eight cognitive, three emotional, and three social factors respectively. Participants were 202 adult learners in corporate training environments. The result indicated that e-Tutor was perceived useful in general. Amongst three, cognitive support was perceived as the most useful. Emotional and social supports of encouraging learners and facilitating interactions among learners were also reported useful to facilitate the desired learning outcomes.

• Journal of the Korean Society of Food Science and Nutrition
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• v.43 no.2
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• pp.179-186
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• 2014

Metabolomics is the study of changes in the metabolic status of an organism as a consequence of drug treatment, environmental influences, nutrition, lifestyle, genetic variations, toxic exposure, disease, stress, etc, through global or comprehensive identification and quantification of every single metabolite in a biological system. Since most chronic diseases have been demonstrated to be linked to nutrition, nutritional metabolomics has great potential for improving our understanding of the relationship between disease and nutritional status, nutrient, or diet intake by exploring the metabolic effects of a specific food challenge in a more global manner, and improving individual health. In particular, metabolite profiling of biofluids, such as blood, urine, or feces, together with multivariate statistical analysis provides an effective strategy for monitoring human metabolic responses to dietary interventions and lifestyle habits. Therefore, studies of nutritional metabolomics have recently been performed to investigate nutrition-related metabolic pathways and biomarkers, along with their interactions with several diseases, based on animal-, individual-, and population-based criteria with the goal of achieving personalized health care in the future. This article introduces analytical technologies and their application to determination of nutritional phenotypes and nutrition-related diseases in nutritional metabolomics.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.14 no.11
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• pp.5771-5777
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• 2013

The need of continuous management for diseases came to the fore as a chronic disease has increased, however, research related to personalized food intake analysis are insufficient. In diabetes risk analysis model of this study, food preferences are calculated by Pearson correlation coefficient that is proven method to assess the similarity, and diabetes risk is computed as a Logistic regression that was used in prevalence studies. For the Significance evaluation of this model, it was verified through t-test at 0.05 level of 52 comparison subjects and 52 control subjects. Both groups were significantly independent (p=0.046 <0.05). This model is a new way to personalized health management, through the application to healthcare system based on web and mobile.

• Journal of Pharmacopuncture
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• v.23 no.2
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• pp.71-78
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• 2020

Objectives: This study was conducted to development of hazardous materials management standards for the decoction type of personalized herbal medicines (PHMs). Methods: This study was conducted in two stages. We searched documents about criteria to use words such as 'Herb', 'Herbal medicine', and 'Botanical Drug' and summarized the results. We organized the committee consisted of seven experts, and held two meetings to reach an agreement on hazardous management standards of the decoction type of PHMs. Results: The seven documents were presented in the literature review and six items related to hazardous management standards of decoction were identified. The second expert meeting brought that a total of six items, including heavy metal, pesticide residues, sulfur dioxide, benzopyrene, mycotoxin, and micro-organism limits, were selected for safety management of decoction type of PHMs. Also, the criteria and test methods for each standard were suggested for monitoring the decoction type of PHMs. Conclusion: The study suggested hazardous material management standards and criteria for the decoction types of PHMs. In the future, it would be necessary to conduct a pilot test to ensure the validity and credibility of the safety management standard and criteria. Furthermore, the government level safety management system should be introduced to verify the safety of decoction medicines.

• Journal of Korean Academy of Nursing
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• v.52 no.5
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• pp.535-549
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• 2022

Purpose: The study aimed to design and develop an automated personalized self-care (APSC) program for patients with type 2 diabetes mellitus. The secondary aim was to present a clinical protocol as a mixed-method research to test the program effects. Methods: The APSC program was developed in the order of analysis, design, implementation, and evaluation according to the software development life cycle, and was guided by the self-regulatory theory. The content validity, heuristics, and usability of the program were verified by experts and patients with type 2 diabetes mellitus. Results: The APSC program was developed based on goal setting, education, monitoring, and feedback components corresponding to the phases of forethought, performance/volitional control, and self-reflection of self-regulatory theory. Using the mobile application, the participants are able to learn from educational materials, monitor their health behaviors, receive weekly-automated personalized goals and feedback messages, and use an automated conversation system to solve the problems related to self-care. The ongoing two-year study utilizes a mixed method design, with 180 patients having type 2 diabetes mellitus randomized to receive either the intervention or usual care. The participants will be reviewed for self-care self-efficacy, health behaviors, and health outcomes at 6, 12, 18, and 24 months. Participants in the intervention group will be interviewed about their experiences. Conclusion: The APSC program can serve as an effective tool for facilitating diabetes health behaviors by improving patients' self-care self-efficacy and self-regulation for self-care. However, the clinical effectiveness of this program requires further investigation.

• Journal of Intelligence and Information Systems
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• v.17 no.2
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• pp.109-117
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• 2011

The exhibition industry, as technology-intensive, eco-friendly industry, contributes to regional and national development and enhancement of its image as well, if it joins cultural and tourist industry. Therefore, We need to revitalize the exhibition industry, as actively holding an exhibition event. However, to attract a number of exhibition audience, the work of enhancing audience satisfaction and awareness of value for participation should be prioritized after improving quality of service within exhibition hall. As one way to enhance the quality of service, it is thought that the way providing personalized service geared toward each audience is needed. that is, if audience avoids the complexity in exhibition space and it affords them service to enable effective time and space management, it will improve the satisfaction. All such personalized service affordable lets the audience's preference on the basis of each audience profile registered in advance online grasp. and Based on this information, it is provided with exhibition-related information suited their purpose that is the booth for the interesting audience, the shortest path to go to the booth and event via audience's smart phone. and it collects audience's reaction information, such as visiting the booth, participating the event through offered the information in this way and location information for the flow of movement, the present position so that it makes revision of existing each audience profile. After correcting the information, it extracts the individual's preference. hereunder, it provides recommend booth and event information. in other words, it provides optimal information for individual by amendment based on reaction information about recommending information built on basic profile. It provides personalized service dynamic and interactive with audience. This paper will be able to provide the most suitable information for each audience through circular and interactive structure and designed smart-phone application supportable for updating dynamic and interactive personalized service that is able to afford surrounding information in real time, as locating movement position through sensing. The proposed application collects user‘s context information and carrys information gathering function collecting the reaction about searched or provided information via sensing. and it also carrys information gathering function providing needed data for user in exhibition hall. In other words, it offers information about recommend booth of position foundation for user, location-based services of recommend booth and involves service providing detailed information for inside exhibition by using service of augmented reality, the map of whole exhibition as well. and it is also provided with SNS service that is able to keep information exchange besides intimacy. To provide this service, application is consisted of several module. first of all, it includes UNS identity module for sensing, and contain sensor information gathering module handling and collecting the perceived information through this module. Sensor information gathered like this transmits the information gathering server. and there is exhibition information interfacing with user and this module transmits to interesting information collection module through user's reaction besides interface. Interesting information collection module transmits collected information and If valid information out of the information gathering server that brings together sensing information and interesting information is sent to recommend server, the recommend server makes recommend information through inference with gathered valid information. If this server transmit by exhibition information process, exhibition information process module is provided with user by interface. Through this system it raises the dynamic, intelligent personalized service for user.

• The Journal of Korean Association of Computer Education
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• v.7 no.6
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• pp.117-128
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• 2004

Compared with traditional face-to-face instruction, online learning causes learners to experience more severe feeling of isolation and results in higher dropout rate. This is due to the lack of interaction, sense of belonging, membership, interdependency, cooperation among members and social environment that enables persistence in online learning. Therefore, it is very important for grouping e-learning community to lower the dropout rate and eliminate feeling of isolation. In this paper, the research has been done on the inclination test list to be applied for grouping the desirable learning community. And on the basis of this research, the grouping system for e-learning community(GSE) based on intelligent multi agents for an inclination test using homogeneous and heterogeneous items has been developed. GSE system has such properties that construct a personalized user profile by an agent, and then make groupings according to users' inclination. When this system was evaluated, about 88% of learners were satisfied, and they wanted the group not to be disorganized but to be maintained.