• Journal of Korea Society of Digital Industry and Information Management
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• v.11 no.3
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• pp.21-29
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• 2015

The Internet of Things (IoT) is an Infrastructure which allows to connect with each device in physical world through the Internet. Thus IoT enables to provide meahup services or intelligent services to human user using collected data from those devices. Due to these advantages, IoT is used in divers service domains such as traffic, distribution, healthcare, and smart city. However, current IoT provides restricted services because it only supports monitor and control devices according to collected data from the devices. To resolve this problem, we propose a design and implementation of personalized IoT service base on service orchestration. The proposed service allows to discover specific services and then to combine the services according to a user location. To this end, we develop a service ontology to interpret user information according to meanings and smartphone web app to use the IoT service by human user. We also develop a service platform to work with external IoT platform. Finally, to show feasibility, we evaluate the proposed system via study.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.12 no.3
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• pp.43-49
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• 2012

Personalized message syndication is an important process for maximizing the effect of mobile marketing. This paper proposes an algorithm for determining clients satisfying target conditions in real-time. The proxy server as an intermediate node stores client profiles (gender, age, location, etc) and their respective summaries into a database. When a company syndicates messages at run time, the proxy server maps target conditions expressed by boolean expressions to integer value and determines target clients by comparing target value with profile summary. Thus, this approach provides efficient personalized message syndication in very large systems with millions of clients because it can determine target clients in real-time and work with a traditional database easily.

• Journal of the Korea Society of Computer and Information
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• v.22 no.12
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• pp.117-123
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• 2017

The personalized search algorithm is a search system that analyzes the user's IP, cookies, log data, and search history to recommend the desired information. As a result, users are isolated in the information frame recommended by the algorithm. This is called 'Filter bubble' phenomenon. Most of the personalized data can be deleted or changed by the user, but data stored in the service provider's server is difficult to access. This study suggests a way to neutralize personalization by keeping on sending random query words. This is to confuse the data accumulated in the server while performing search activities with words that are not related to the user. We have analyzed the rank change of the URL while conducting the search activity with 500 random query words once using the personalized account as the experimental group. To prove the effect, we set up a new account and set it as a control. We then searched the same set of queries with these two accounts, stored the URL data, and scored the rank variation. The URLs ranked on the upper page are weighted more than the lower-ranked URLs. At the beginning of the experiment, the difference between the scores of the two accounts was insignificant. As experiments continue, the number of random query words accumulated in the server increases and results show meaningful difference.

• Journal of Intelligence and Information Systems
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• v.18 no.2
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• pp.131-141
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• 2012

In this paper, we propose a new method for collaborative filtering (CF)-based recommender systems. Traditional CF-based recommendation algorithms have applied constant settings such as a reference group (neighborhood) size and a significance level to all users. In this paper we develop a new method that identifies optimal personalized settings for each user and applies them to generating recommendations for individual users. Personalized parameters are identified through iterative simulations with 'training' and 'verification' datasets. The method is compared with traditional 'constant settings' methods using Netflix data. The results show that the new method outperforms traditional, ordinary CF. Implications and future research directions are also discussed.

• Proceedings of the Korea Information Processing Society Conference
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• 2014.04a
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• pp.981-984
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• 2014

개인화된 서술형 수학 문제(mathematics word problem)는 오랫동안 연구된 분야로 학생들의 학업 성취도와 수학에 대한 태도에 관심을 가져왔다. 본 연구에서는 2013년 도입된 스토리텔링 수학에 개인화된 콘텐츠를 접목하여 그 효과를 알아보고자 하였다. 초등학생 26명을 대상으로 하여 약 110분 동안 수업을 진행하였으며, 무게에 대한 새로운 개념을 배우는 데 그 목적을 두었다. 각각 13명씩 개인화 그룹과 비 개인화 그룹으로 나누어 수업을 진행하였다. 학업 성취도(Learning Achievement)에서는 사전 시험(pre-test) 점수가 너무 높아 두 그룹 간에 서로간의 유의한 차이점을 발견하지 못했다. 수학에 대한 태도 부분과 몰입도(Flow) 부분에서는 다소 개인화 그룹의 값이 높았지만, 통계적으로 유의한 정도는 차이는 아니었다. 하지만 정성적 분석에서는 차이가 있었다. 개인화 그룹(Personalized group)은 비 개인화 그룹(non-personalized group)에 비해 개인화(personalization)가 수업의 재미있는 요소로서 보다 중요한 작용을 했다고 느꼈다. 또한, 테스트나 측정(measure) 부분에서 생겼던 문제점을 개선하여 재 실험이 있을 시엔 유의미한 값을 나타낼 것으로 기대된다.

• Journal of information and communication convergence engineering
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• v.20 no.4
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• pp.288-294
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• 2022

The media content market has been growing, as various types of content are being mass-produced owing to the recent proliferation of the Internet and digital media. In addition, platforms that provide personalized services for content consumption are emerging and competing with each other to recommend personalized content. Existing platforms use a method in which a user directly inputs video metadata. Consequently, significant amounts of time and cost are consumed in processing large amounts of data. In this study, keyframes and audio spectra based on the YCbCr color model of a movie trailer were extracted for the automatic generation of metadata. The extracted audio spectra and image keyframes were used as learning data for genre recognition in deep learning. Deep learning was implemented to determine genres among the video metadata, and suggestions for utilization were proposed. A system that can automatically generate metadata established through the results of this study will be helpful for studying recommendation systems for media super-personalization.

• The Journal of Korean Association of Computer Education
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• v.9 no.1
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• pp.71-80
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• 2006

As e-commerce developing rapidly, it is becoming a research focus about how to find customer's behavior patterns and realize commerce intelligence by use of Web mining technology. One of the most successful and widely used technologies for building personalization and goods recommendation system is collaborating filtering. However, collaborative filtering have serious data sparsity problem. Traditional association rule does not consider user's interests or preferences to provide a user with specific personalized service.In this paper, we propose an goods recommendation system, which is integrated an collaborative filtering algorithm with item-to-item corelation and an improved Apriori algorithm. This system has user's interests or preferences ro provide a user with specific personalized service.

• Journal of the Korea Convergence Society
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• v.10 no.11
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• pp.159-164
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• 2019

Recent developments in ICT technology have increased interest in the care and health of pets such as dogs and cats. In this paper, cluster analysis was performed based on the component data of pet food to be used in various fields of the pet industry. For cluster analysis, the similarity was analyzed by analyzing the correlation between components of 300 dogs and cats in the market. In this paper, clustering techniques such as Hierarchical, K-Means, Partitioning around medoids (PAM), Density-based, Mean-Shift are clustered and analyzed. We also propose a personalized recommendation system for pets. The results of this paper can be used for personalized services such as feed recommendation system for pets.

• Journal of the Korea Society of Computer and Information
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• v.26 no.5
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• pp.61-68
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• 2021

In this paper, we propose an edge computing-based exercise data collection device that can be provided for exercise rehabilitation services. In the existing cloud computing method, when the number of users increases, the throughput of the data center increases, causing a lot of delay. In this paper, we design and implement a device that measures and estimates the position of keypoints of body joints for movement information collected by a 3D camera from the user's side using edge computing and transmits them to the server. This can build a seamless information collection environment without load on the cloud system. The results of this study can be utilized in a personalized rehabilitation exercise coaching system through IoT and edge computing technologies for various users who want exercise rehabilitation.

• Journal of Science and Technology Studies
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• v.14 no.1
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• pp.139-175
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• 2014

For the past 20 years, expert groups and citizens in Korea have debated on the usefulness of personalized medicine. These debates were mainly focussed on the possibility of the promise - people mainly discussed whether it was a probable future or if it was just a hype. Following Hedgecoe and Tutton(2002) who argue that it is only a 'rhetorial device', we will analyze about 9,000 news media coverages that deal with personalized medicine. With these data, we will show that the same terminology of personalized medicine have been used very differently according to the time and people who use it. Our research will show that this term has both diachronic heterogeneity and synchronic equivocality. This has happened because of the innate lack that exists in our symbolic system. Policy and governance regarding new technology is important because they provide quilting point to those slippery term/signifiant. Also we would like to carefully suggest that we might be able to call this phenomena as signifiant-politics.