• Journal of the Korea Convergence Society
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• v.9 no.3
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• pp.231-240
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• 2018

Recently, personal information processing are becoming more important in the behavioral advertising based on online and mobile platform. The behavioral advertising analyzes and utilizes individual's search & purchase history, hobbies, and tendency based on the personal behavior information collected using the automatic collection device. Therefore, it collects and stores other types of personal information which did't defined in Privacy Act and can analyze personal behavior. This characteristics may cause disclosure of personal information and exposure to intrusion. In this paper, we investigate and analyze the privacy policy of the advertising agencies, and discussded the measures to be taken in collecting, storing and using personal information suitable for behavior information.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.2
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• pp.185-190
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• 2006

Most systems in ubiquitous computing analyze context information of users which have similar propensity with demographics methods and collaborative filtering to provide personalized recommendation services. The systems have mostly used static context information such as sex, age, job, and purchase history. However the systems have limitation to analyze users' propensity accurately and to provide personalized recommendation services in real-time, because they have difficulty in considering users situation as moving path. In this paper we use users' moving path of dynamic context to consider users situation. For the prediction accuracy we complete with a path completion algorithm to moving path which is inputted to RSOM. We train the moving path to be completed by RSOM, analyze users' moving pattern and predict a future moving path. Then we recommend the nearest product on the prediction path with users' high preference in real-time. As the experimental result, MAE is lower than 0.5 averagely and we confirmed our method can predict users moving path correctly.

• Journal of the Korea Society of Computer and Information
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• v.17 no.2
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• pp.197-206
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• 2012

A exisiting recommedation system using association rules has the problem, such as delay of processing speed from a cause of frequent scanning a large data, scalability and accuracy as well. In this paper, using a Implicit method which is not used user's profile for rating, we propose the personalized recommendation system which is a new method using the FP-tree mining based on RFM. It is necessary for us to keep the analysis of RFM method and FP-tree mining to be able to reflect attributes of customers and items based on the whole customers' data and purchased data in order to find the items with high purchasability. The proposed makes frequent items and creates association rule by using the FP-tree mining based on RFM without occurrence of candidate set. We can recommend the items with efficiency, are used to generate the recommendable item according to the basic threshold for association rules with support, confidence and lift. To estimate the performance, the proposed system is compared with existing system. As a result, it can be improved and evaluated according to the criteria of logicality through the experiment with dataset, collected in a cosmetic internet shopping mall.

• The Journal of Society for e-Business Studies
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• v.26 no.1
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• pp.127-140
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• 2021

Recently, sequence data containing time information, such as sensor measurement data and purchase history, has been generated in various applications. So far, many methods for finding sequences that are significantly different from other sequences among given sequences have been proposed. However, most of them have a limitation that they consider only the order of elements in the sequences. Therefore, in this paper, we propose a new anomalous sequence detection method that considers both the order of elements and the time interval between elements. The proposed method uses an extended LSTM autoencoder model, which has an additional layer that converts a sequence into a form that can help effectively learn both the order of elements and the time interval between elements. The proposed method learns the features of the given sequences with the extended LSTM autoencoder model, and then detects sequences that the model does not reconstruct well as anomalous sequences. Using experiments on synthetic data that contains both normal and anomalous sequences, we show that the proposed method achieves an accuracy close to 100% compared to the method that uses only the traditional LSTM autoencoder.

• Journal of Intelligence and Information Systems
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• v.18 no.4
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• pp.19-42
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• 2012

Consumer consumption patterns are shifting rapidly as buyers migrate from offline markets to e-commerce routes, such as shopping channels on TV and internet shopping malls. In the offline markets consumers go shopping, see the shopping items, and choose from them. Recently consumers tend towards buying at shopping sites free from time and place. However, as e-commerce markets continue to expand, customers are complaining that it is becoming a bigger hassle to shop online. In the online shopping, shoppers have very limited information on the products. The delivered products can be different from what they have wanted. This case results to purchase cancellation. Because these things happen frequently, they are likely to refer to the consumer reviews and companies should be concerned about consumer's voice. E-commerce is a very important marketing tool for suppliers. It can recommend products to customers and connect them directly with suppliers with just a click of a button. The recommender system is being studied in various ways. Some of the more prominent ones include recommendation based on best-seller and demographics, contents filtering, and collaborative filtering. However, these systems all share two weaknesses : they cannot recommend products to consumers on a personal level, and they cannot recommend products to new consumers with no buying history. To fix these problems, we can use the information which has been collected from the questionnaires about their demographics and preference ratings. But, consumers feel these questionnaires are a burden and are unlikely to provide correct information. This study investigates combining collaborative filtering with the centrality of social network analysis. This centrality measure provides the information to infer the preference of new consumers from the shopping history of existing and previous ones. While the past researches had focused on the existing consumers with similar shopping patterns, this study tried to improve the accuracy of recommendation with all shopping information, which included not only similar shopping patterns but also dissimilar ones. Data used in this study, Movie Lens' data, was made by Group Lens research Project Team at University of Minnesota to recommend movies with a collaborative filtering technique. This data was built from the questionnaires of 943 respondents which gave the information on the preference ratings on 1,684 movies. Total data of 100,000 was organized by time, with initial data of 50,000 being existing customers and the latter 50,000 being new customers. The proposed recommender system consists of three systems : [+] group recommender system, [-] group recommender system, and integrated recommender system. [+] group recommender system looks at customers with similar buying patterns as 'neighbors', whereas [-] group recommender system looks at customers with opposite buying patterns as 'contraries'. Integrated recommender system uses both of the aforementioned recommender systems to recommend movies that both recommender systems pick. The study of three systems allows us to find the most suitable recommender system that will optimize accuracy and customer satisfaction. Our analysis showed that integrated recommender system is the best solution among the three systems studied, followed by [-] group recommended system and [+] group recommender system. This result conforms to the intuition that the accuracy of recommendation can be improved using all the relevant information. We provided contour maps and graphs to easily compare the accuracy of each recommender system. Although we saw improvement on accuracy with the integrated recommender system, we must remember that this research is based on static data with no live customers. In other words, consumers did not see the movies actually recommended from the system. Also, this recommendation system may not work well with products other than movies. Thus, it is important to note that recommendation systems need particular calibration for specific product/customer types.

• Culinary science and hospitality research
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• v.19 no.1
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• pp.85-102
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• 2013

The purpose of this study is to examine the effect relation of the external variables of the RFID for eco-agricultural products, that of the external variables on TAM approachableness and usefulness, that of TAM approachableness on usefulness, that of TAM as approachableness and usefulness on trust and behavior intention, and the effect of trust on the behavior intention. The subjects of the actual analysis are the ordinary consumers aged 20 and over living in Seoul and Gyeong-gi Province and have the experience of purchasing eco-agricultural products. Total 300 copies of questionnaire were distributed for 14 days from September 24, 2012 until October 7, 2012, and total 278(92.7%) copies of survey materials were used for the final statistical analysis data except some found too strong unequal distribution of the response value or the value unknown at present. In the analysis results of this study, by examining the effects of the external variables of the RFID for eco-agricultural products on TAM's accessibility and usefulness and verifying the causal relationship between TAM's accessibility and consumers' trust and behavioral intent, this study has a sufficient value as an initial study on the RFID for eco-agricultural products. Thus, the results of this in-depth study show that producers and distributors maintaining and providing with safer eco-agricultural products food directly influence service companies' visible achievements.

• Journal of Convergence Society for SMB
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• v.6 no.3
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• pp.79-83
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• 2016

The purpose of this study is to provide customized information to student which study and live in a university campus. In this study, we collect internal data of campus and external data on the Internet such as the blog or SNS and, then store and process them. After that, we propose a system for providing individual students one-to-one marketing by analyzing these data in detail. This system analyzes purchase history information and the attendance of the building, and then transmits the coupon and information individually according to the pattern to the student's mobile phone.

• Proceedings of the Korean Information Science Society Conference
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• 2004.04b
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• pp.1-3
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• 2004

산업설비 현장에서는 설비 운영과 관련하여 재료 관련 정보에 대한 요구가 비교적 빈번하게 발생하고 있으나 필요한 기술 자료의 확보, 검토 등에 많은 어려움을 겪고 있다. 따라서 산업설비에 적용되는 재료들을 종합하여 비교, 검토할 수 있는 시스템을 개발하여 인터넷을 통해 제공할 경우 산업설비의 운영효율 향상, 신뢰도 향상에 기여할 수 있을 것으로 기대된다. 재료 데이터베이스 시스템은 재료가 사용되는 설비의 운영과 관련한 각종 데이터와 상호 연계되어야 할 필요가 있다. 손상해석, 재료선정 등은 인장시험 등 각종 시험 데이터와 더불어 재료가 사용되는 실제 설비의 응력. 온도 등 운전환경, 사용이력 등이 중요한 판단 자료가 되기 때문이다. 또한 재료 데이터는 플랜트 운영시스템, 자재구매/관리시스템 등 다양한 시스템의 기본 마스터데이터로 이용들 수 있다. 따라서 재료 데이터가 갖는 이러한 특성을 충족시키기 위해서는 확장성과 이식성이 뛰어난 XML을 기반으로 재료 데이터베이스를 개발하는 것이 바랑직할 것으로 판단된다. XML을 기반으로 개발된 재료 데이터베이스는 20,000건의 방대한 재료 규격정보를 담고 있으며 규격검색, 이름검색, 화학성분 검색, 기계적 특성 검색 등 다양한 검색 기능을 제공한다. 또한 대표적인 종합플랜트인 화력발전소의 설iii데이터와 통할 연동되어 재료의 적용현황 및 환경 등을 통합적으로 검색할 수 있다. XML을 이용한 본 개발시스템은 향후 상용 ERP 패키지 다양한 시스템의 기본 데이터베이스로 활용될 수 있을 것으로 기대된다.

• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.1
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• pp.193-200
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• 2022

In this study, the intellectual property rights of digital contents (creations) are protected by using block chain technology that cannot be damaged or forged. So, we build a blockchain-based content sales revenue tracking system and platform that activates the transaction and distribution of digital content (creation). We developed an API server that can be used for content registration and revision history management smart contract, license management smart contract according to content purchase, content inquiry function through files and hashes, and web and APP services. Through this, it is possible to prove the relationship between the rights of the creators of digital content creations and protect the rights of the creators.

• Proceedings of the Korea Information Processing Society Conference
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• 2020.05a
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• pp.314-317
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• 2020

환자의 생체신호 측정 및 관찰, 영상 위생 등을 포함하는 직접간호는 간호사들의 총 간호활동 시간 중 내과는 48%, 외과는 40% 로 간호사들의 업무 부담이 되고 있다. 또한 의료기관에서 사용되는 의료기기들은 여러 회사에서 구매하여 사용되기 때문에 각 회사마다 상이한 프로토콜을 가지고 있어 하나의 시스템으로 생체신호를 모으기가 쉽지 않다. 따라서 여러 장비에서 생체신호를 실시간으로 취득하여 통합 관리할 수 있는 시스템 개발을 통해 간호사의 직접간호 업무량을 줄여 간호사의 근무환경 개선뿐만 아니라 중증환자의 경우 환자 생체신호에 대한 실시간 원격감시가 가능하고 환자에게서 발생된 모든 생체신호가 데이터베이스 시스템으로 기록관리 됨으로 인해 환자의 생체 신호에 대한 이력 추적관리가 가능함으로써, 양질의 의료 서비스가 가능한 환자케어시스템을 개발하고자 한다.