• Asia pacific journal of information systems
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• v.14 no.2
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• pp.131-149
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• 2004

Collaborative filtering is one of popular techniques for personalized recommendation in e-commerce sites. An advantage of collaborative filtering is that the technique can work with sparse evaluation data to predict preference scores of new alternative contents or advertisements. There is, however, no in-depth study about the sparsity effect of customer's evaluation data to the performance of recommendation. In this study, we investigate the sparsity effect and hybrid usages of customers' evaluation data and purchase data using an experiment result. The result of the analysis shows that the performance of recommendation decreases monotonically as the sparsity increases, and also the hybrid usage of two different types of data; customers' evaluation data and purchase data helps to increase the performance of recommendation in sparsity situation.

• Journal of Digital Convergence
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• v.5 no.2
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• pp.71-81
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• 2007

This study is to design the web-based educational setting in industry to test the effectiveness of online teaching in Korea. Especially this study focuses on the impact of information technology on educational influence on employees in small and medium-sized enterprises (SME) because these experimental settings are differed from universities or large firms often called Jaebaels. The findings of the study are summarized as education setting especially personalization content materials greatly influences on the speed and quality of employees' learning. Also the positive attitudes of employees toward the learning environment correlate to the effectiveness of online learning in SME.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.10 no.1
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• pp.49-53
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• 2010

e-learning and m-learning have some problems that data transmission frequently discontinuously, communication cost increases, the computation speed of mass data drops, battery limitation in the mobile learning environments. In this paper, we propose the PULIMS for u-learning systems. The proposed system intellectualize the education environment using intelligent mobile agent, supports the customized education service, and helps that learners feasible access to the education information through mobile phone. We can see the fact that the efficience of proposed method is outperformed that of the conventional methods. The PULIMS is new technology that can be used to learn whenever and wherever learners want in Ubiquitous education environment.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2002.05a
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• pp.342-351
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• 2002

A personalized product recommendation is an enabling mechanism to overcome information overload occurred when shopping in an Internet marketplace. Collaborative filtering has been known to be one of the most successful recommendation methods, but its application to e-commerce has exposed well-known limitations such as sparsity and scalability, which would lead to poor recommendations. This paper suggests a personalized recommendation methodology by which we are able to get further effectiveness and quality of recommendations when applied to an Internet shopping mall. The suggested methodology is based on a variety of data mining techniques such as web usage mining, decision tree induction, association rule mining and the product taxonomy. For the evaluation of the methodology, we implement a recommender system using intelligent agent and data warehousing technologies.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2002.11a
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• pp.597-600
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• 2002

새 천년이 시작하면서 자본주의의 새로운 기업 모델로 e-Biz가 각광받았다. 하지만 곧바로 사양되었으며, 이 시점에서 새로운 대안으로 떠오른 것이 바로 모바일 인터넷 비즈니스였다. M-Biz는 편재성(Ubiquity), 접근성(Reachability), 보안성(Security), 위치확인(Localiz ation), 즉시 연결성(Instant Connectivity), 개인화(Personalization)의 모바일 인터넷 장점을 이용하여 뚜렷한 수익모델로 가능해졌기 때문이다. 이런 모바일 컨텐츠 서비스는 정보제공(Information), Entertainment, 상거래(Transaction), 단문메시지 (Communication) 등을 현재 제공하고 있으며, 이러한 기능들을 현재 개발되고 있는 홈 오토메이션 시스템과 결합하여 새로운 형태의 홈 오토메이션을 기반으로 하는 모바일 서비스 지원 기법을 본 논문에서는 논하였다.

• Journal of Information Technology Applications and Management
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• v.13 no.4
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• pp.155-165
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• 2006

Along with the evolution of wireless internet environment, information services in mobile internet are being explosively increased. However, due to the high expense for the use of those services, it is not easy to collect the devoted customers. In order to overcome the adverse conditions, the service should be supported by a strategic application system which facilitates easier and fast service for customer's information needs than that of traditional on-line internet. In this paper, we investigate the strategy how to provide the information service under the customer satisfaction, and also consider some ways of personalization of information service and information provision in real-time. As a result of the works, we develop a system, MoSA-CRM(Mobile Service Application based on CRM) to support customer-oriented information service. We believe that the MoSA-CRM system can provide some benefits for information providers in mobile e-commerce market such as flexibility in business strategy, systematic customer relationship management, and growth in financial ability.

• Journal of KIISE:Software and Applications
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• v.28 no.5
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• pp.431-439
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• 2001

최근 본격적으로 서비스되기 시작한 비교쇼핑몰들은 번거로운 쇼핑과정을 대신하여 한 사이트 내에서 원하는 상품에 대한 통합검색과 비교가 한번에 이루어지게 해준다. 그러나 에이전트에 의해 수집된 상품정보의 양이 방대해진 반면 고객중심의 one-to-one marketing이 이루어지지 않아 불필요한 정보로 인해 여전히 쇼핑의 효율이 낮다. 본 논문에서는 등록된 각 고객의 프로파일과 관심도에 따라 코드화된 정보의 처리를 통하여 개별적인 상품정보의 제공과 한 번의 클릭으로 원하는 쇼핑몰의 원하는 상품만을 비교해 보여줄 수 있는 멀티 에이전트 기반의 개인화된 비교쇼핑 시스템을 제안한다. 또한 학습된 고객의 관심도와 프로파일을 이용하여 구매가 예측되는 상품을 적절한 시간에 e-mail 푸쉬 서비스로 추천 및 광고하여 추가 쇼핑으로 이어질 수 있게 하였다.

• Electronics and Telecommunications Trends
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• v.34 no.3
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• pp.23-33
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• 2019

Broadcast is evolving into media service aimed at user customization, personalization, and participation with high-quality broadcasting contents (4K/8K/AR/VR). A broadcast infrastructure is needed to engage with the competition for providing large-scaled media traffic process, platform performance for adaptive transcoding to diverse receivers, and intelligent service. Cloud service and virtualization in broadcast are becoming more valuable as the broadcasting environment changes and new high-level broadcasting services emerge. This document describes the examples of cloud and virtualization in the broadcast industry, and prospects the network virtualization of broadcast transmission infrastructure, especially terrestrial and cable networks.

• Proceedings of the Korea Information Processing Society Conference
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• 2008.11a
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• pp.1170-1172
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• 2008

본 논문에서는 적응적 이러닝 시스템을 구현하기에 다소 미흡하였던 SCORM 2004 국제표준을 수정한 새로운 표준을 제안한다. 학습자가 선택하는 과목과 학습객체에 대한 데이터 모델을 분리하였고, 이것을 이용한 API 메소드를 추가하였다. 또한 학습객체정보, 그것들의 관계와 과목을 구성하는 학습객체들의 경로에 대한 정보를 설명하는 섹션을 Manifest 파일에 추가하였다. 기존 SCORM 2004 를 수정한 새로운 표준을 따르면 학습자가 과목을 선택하는 시점과 학습 객체가 선택되는 시점을 분리할 수 있고, 기존 학습객체 단위보다 상위 단위의 학습객체 시퀀싱이 가능하게 되어 효율적인 적응적 이러닝 시스템을 구현할 수 있게 된다.

• Journal of Korean Institute of Industrial Engineers
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• v.39 no.2
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• pp.119-128
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• 2013

Most internet users utilize internet portal search engines, such as Naver, Daum and Google nowadays. But since the results of internet portal search engines are based on universal criteria (e.g. search frequency by region or country), they do not consider personal interests. Namely, current search engines do not provide exact search results for homonym or polysemy because they try to serve universal users. In order to solve this problem, this research determines keyword importance and weight value for each individual search characteristics by collecting and analyzing customized keyword at external database. The customized keyword weight values are integrated with search engine results (e.g. PageRank), and the search ranks are rearranged. Using 50 web pages of Goolge search results for experiment and 6 web pages for customized keyword collection, the new customized search results are proved to be 90% match. Our personalization approach is not the way that users enter preference directly, but the way that system automatically collects and analyzes personal information and then reflects them for customized search results.