• Journal of Korea Multimedia Society
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• v.20 no.2
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• pp.322-329
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• 2017

Recently, the importance of personalized healthcare(wellness) services is increasing in the era of the 4th Industrial Revolution. However, the authoring of wellness contents fused with variety of contents and the study of the system which provides the customized recommendation are insufficient. In this paper, we proposes the recommendation service system for the customized convergence wellness contents. The proposed system makes to the wellness contents by the existing cultural/tourism/leisure contents and recommends the customized wellness contents based on a user's profile and the situation information such as location and weather. The proposed systems is expected to contribute to designing the innovative and new service models for the tailored wellness content.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.9 no.6
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• pp.609-616
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• 2016

This paper is proposed a customized market analysis method using SVM and collaborative filtering. The proposed customized market analysis scheme is consists of DC(Data Classification) module, ICF(Improved Collaborative Filtering) module, and CMA(Customized Market Analysis) module. DC module classifies the characteristics of on-line and off-line shopping mall and traditional markets into price, quality, and quantity using SVM. ICF module calculates the similarity by adding age weight and job weight, and generates network using the similarity of purchased item each users, and makes a recommendation list of neighbor nodes. And CMA module provides the result of customized market analysis using the data classification result of DC module and the recommendation list of ICF module. As a result of comparing the proposed customized recommendation list with the existing user based recommendation list, the case of recommendation list using the existing collaborative filtering scheme, precision is 0.53, recall is 0.56, and F-measure is 0.57. But the case of proposed customized recommendation list, precision is 0.78, recall is 0.85, and F-measure is 0.81. That is, the proposed customized recommendation list shows more precision.

• Journal of the Korean Society of Clothing and Textiles
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• v.43 no.3
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• pp.349-360
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• 2019

This study analyzes the trends of recommendation services for customized fashion styles in relation to artificial intelligence. To achieve this goal, the study examined filtering technologies of collaborative, content based, and deep-learning as well as analyzed the characteristics of recommendation services in the users' purchasing process. The results of this study showed that the most universal recommendation technology is collaborative filtering. Collaborative filtering was shown to allow intuitive searching of similar fashion styles in the cognition of need stage, and appeared to be useful in comparing prices but not suitable for innovative customers who pursue early trends. Second, content based filtering was shown to utilize body shape as a key personal profile item in order to reduce the possibility of failure when selecting sizes online, which has limits to being able to wear the product beforehand. Third, fashion style recommendations applied with deep-learning intervene with all user processes of buying products online that was also confirmed to penetrate into the creative area of image tag services, virtual reality services, clothes wearing fit evaluation services, and individually customized design services.

• Journal of the Korea Society of Computer and Information
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• v.22 no.3
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• pp.35-43
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• 2017

In IoT(Internet of Things) environment, users want to receive customized service by users' personal device such as smart watch and pendant. To fulfill this requirement, the mobile device should support a lot of functions. However, the miniaturization of mobile devices is another requirement and has limitation such as tiny display. limited I/O, and less powerful processors. To solve this limitation problem and provide customized service to users, this paper proposes a collaboration system for sharing various computing resources. The paper also proposes the method for reasoning and recommending suitable resources to compose the user-requested service in small device with limited power on expected time. For this goal, our system adopts MBTI(Myers-Briggs Type Indicator) to analyzes user's behavior pattern and recommends personalized resources based on the result of the analyzation. The evaluation in this paper shows that our approach not only reduces recommendation time but also increases user satisfaction with the result of recommendation.

• Journal of Korea Multimedia Society
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• v.23 no.8
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• pp.1075-1087
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• 2020

Aviation ICT technology is a convergence technology between aviation and electronics, and has a wide variety of applications, including navigation and education. Among them, in the field of aerial pilot training, there are many problems such as the possibility of accidents during training and the lack of coping skills for various situations. This raises the need for a simulated pilot training system similar to actual training. In this paper, pilot training data were collected in pilot training system using VR/AR to increase immersion in flight training, and Customized Pilot Training Platform with Collaborative Deep Learning in VR/AR Environment that can recommend effective training courses to pilots is proposed. To verify the accuracy of the recommendation, the performance of the proposed collaborative deep learning algorithm with the existing recommendation algorithm was evaluated, and the flight test score was measured based on the pilot's training data base, and the deviations of each result were compared. The proposed service platform can expect more reliable recommendation results than previous studies, and the user survey for verification showed high satisfaction.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.84-86
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• 2022

Task-oriented chatbots prevail in various filed with the artificial intelligent dialogue system. The need for chatbots in customer services is growing, especially in education businesses given that there are many user inquiries and consultation requests. However, current dialogue systems only function as simple reactions or predetermined and frequently used actions. Meanwhile, the research about customized recommendation systems through artificial intelligence is very active with a wide variety of educational content. Although a dialogue system and a recommendation system is a core element in this domain, it has a limitation in that it is being conducted separately. Therefore, we present a study on a recommendation system that can recommend user-customized lectures combined with a dialogue system. With this combination, our system can respond to additional functions beyond these limitations. Through our research, we expect that work efficiency and user satisfaction will be improved by applying chatbots in education domains that are becoming more diversified and personalized.

• Journal of Distribution Science
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• v.15 no.2
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• pp.15-20
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• 2017

Purpose - This paper aims to suggest a delivery constrained internet shopping optimization problem (DISOP) which must be solved for online recommendation system to provide a customized service considering cost and delivery conditions at the same time. Research design, data, and methodology - To solve a (DISOP), we propose a multi-objective formulation and a solution approach. By using a commercial optimization software (LINDO), a (DISOP) can be solved iteratively and a pareto optimal set can be calculated for real-sized problem. Results - We propose a new research problem which is different with internet shopping optimization problem since our problem considers not only the purchasing cost but also delivery conditions at the same time. Furthermore, we suggest a multi-objective mathematical formulation for our research problem and provide a solution approach to get a pareto optimal set by using numerical example. Conclusions - This paper proposes a multi-objective optimization problem to solve internet shopping optimization problem with delivery constraint and a solution approach to get a pareto optimal set. The results of research will contribute to develop a customized comparison and recommendation system to help more easy and smart online shopping service.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.10a
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• pp.300-301
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• 2016

This paper proposes customized recommendation algorithm to improve the QoS(quality of service) of sport for all sports content uses to user profile and team grade. The proposed recommendation module is based on user profile information, and it recommends suitable team contents to user with Euclidean distance algorithm and preference weights between teams.

• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.2
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• pp.339-346
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• 2021

In this paper, we implemented an interface that provides a better food ordering mechanism and enables real-time selection of recipe ingredient ratios for customized food orders from global customers. Providing appropriate food to global customers by arranging a selection of menu on the order system screen that shows the basic ratio of each recipe ingredient and provides a customized recipe ingredient composition ratio by configuring a recipe graph without a system for simply selecting and ordering food menus. By enabling interaction, it allows users to provide customized services through the ratio adjustment of various recipe ingredients in the food menu ordering device

• Journal of the Korea Society of Computer and Information
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• v.16 no.9
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• pp.19-26
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• 2011

Users want to receive customized service using users' personal device. To fulfill this requirement, the mobile device has to support a lot of functions. However, the mobile device has limitations such as tiny display screens. To solve this limitation problem and provide customized service to users, this paper proposes the environment to provide services by sharing resources and the method to recommend user-suitable resources among sharable resources. For the resource recommendation, This paper analyzes user's behavior pattern from usage history and proposes the method for recommending customized resources. This paper also shows that the approach is reasonable one for resource recommendation through the satisfaction evaluation.