• The KIPS Transactions:PartD
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• v.11D no.7 s.96
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• pp.1537-1542
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• 2004

Various studies to increase customer satisfaction of a web based system are performed actively. Also in recent days an interest about the personalization that supporting a order type service on customer's viewpoint was raised. So the studies supporting the personalization is required in a web-based marketing system. In this study, we designed an intelligent recommendation system which supporting one to one web marketing using cross selling. The proposed system used an intelligent data mining method as a concurrent cross selling and a sequential cross selling. Also, In experiment on the prototype, we show a proposed system was usable in an practical system applying the mining result.

• KIISE Transactions on Computing Practices
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• v.21 no.11
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• pp.706-712
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• 2015

These days, much information is provided as a list of summaries through mobile services. In this regard, users consume information in which they are interested by observing the list and not by expressing their interest explicitly or implicitly through rating content or clicking links. Therefore, to appropriately model a user's interest, it is necessary to detect latent interest content. In this study, we propose a method for reasoning latent interest of a user by analyzing mobile content consumption logs of the user. Specifically, since erroneous reasoning will drastically degrade service quality, a unanimity ensemble method is adopted to maximize precision. In this method, an item is determined as the subject of latent interest only when multiple classifiers considering various aspects of the log unanimously agree. Accurate reasoning of latent interest will contribute to enhancing the quality of personalized services such as interest-based recommendation systems.

• The Journal of the Korea Contents Association
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• v.10 no.6
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• pp.43-48
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• 2010

Useful emails influence on consumers' purchase behavior and activate them to visit retail stores. Regular contact with consumers by e-mail has positive effects on brand loyalty. However, email marketing has a limitation. Spam now accounts for over half of all e-mail traffic. The increase of email users has resulted in the dramatic increase of spam emails during the past few years. In this paper, we proposed an ontology-based system offering personalized email services to overcome such limitation. Our method is not the ontology-driven spam filtering, but a personalized content service considering personal interests and relations among people by using FOAF and domain ontologies. Our system was successfully tested in email marketing domain.

• Proceedings of the Korean Information Science Society Conference
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• 2001.04b
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• pp.436-438
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• 2001

인터넷은 정보의 바다로 표현할 만큼 방대하며, 이러한 넘치는 정보 속에서 사용자에게 필요한 정보들을 추출하여 사용자들의 효율성과 만족도를 높이는 것이 개인화 정책이고, 결과적으로 전자상거래 사이트에서의 판매의 증가를 이루기 위해 필요한 것이다. 따라서 개개인의 특성에 맞춘 개인화 서비스가 현재의 인터넷에서 제공하는 효율성을 뛰어넘을 수 있는 새로운 해결점으로 주목받고 있다. 본 논문에서는 협력 필터링(Collaborative filtering) 방법을 사용하여 사용자의 선호도(preference)를 결정하고, 이를 토대로 웹페이지의 콘텐트를 재 설계하고, 알맞은 아이템 추천 서비스를 사용자에게 제공하는 협력 필터링 에이전트(Collaborative Filtering Agent)를 제안하고자 한다. 이를 통하여 기존의 사용자 또는 처음 방문한 사용자에게도 사이트를 방문하는데 만족도와 효율성을 높이도록 하는 것이 목표이다.

• Journal of Broadcast Engineering
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• v.11 no.3 s.32
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• pp.284-301
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• 2006

In this paper, we present a design and implementation of a TV-Anytime metadata authoring tool for providing personalized data-broadcasting services. The TV-Anytime specifies metadata schema, metadata coding and delivery, and provides service models to provide personalized broadcasting content services at anytime when users want to consume using metadata including ECG (Electronic Content Guide) and content descriptive information in a PDR (Personal Digital Recorder)-centric environment. In spite of a useful services based on TV-anytime metadata, the metadata authoring still remains as a harassing and time consuming task. For easy metadata authoring, the proposed metadata authoring provides the following key functionalities: metadata visualization, media access, and semi-automatic method for editing segment related metadata.

• Proceedings of the Korea Society of Information Technology Applications Conference
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• 2002.11a
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• pp.425-432
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• 2002

The purpose of this study is to help implement KISTI knowledge management systems through analysis of ordering which is most important among system characteristics. This study uses Delphi technique to specialist in science and technology knowledge content area for empirical analysis. The result of this study is as fellowed First the most important characteristic is retrieval function in systems. Second knowledge management systems basic characteristics like as retrieval function storage/retention function accessibility, easy of use, classification function lank high position while additional characteristics like as knowledge linkage knowledge evaluation personalization communication supporting, multimedia supporting lank low position.

• Proceedings of the Korea Society for Industrial Systems Conference
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• 2002.11a
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• pp.425-432
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• 2002

The purpose of this study is to help implement KISTI knowledge management systems through analysis of ordering which is most important among system characteristics. This study uses Delphi Technique to specialist in science and technology knowledge content area for empirical analysis. The result of this study is as followed First, the most important characteristic is retrieval function in systems. Second knowledge management systems basic characteristics like as retrieval function storage/retention function, accessibility, easy of usa classification function lank high position while additional characteristics like as knowledge linkage, knowledge evaluation, personalization, communication supporting, multimedia supporting lank low position.

• Journal of KIISE:Software and Applications
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• v.34 no.6
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• pp.539-548
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• 2007

Mobile content adaptation is a technology of effectively representing the contents originally built for the desktop PC on wireless mobile devices. Previous approaches for Web content adaptation are mostly device-dependent. Also, the content transformation to suit to a smaller device is done manually. Furthermore, the same contents are provided to different users regardless of their individual preferences. As a result, the user has difficulty in selecting relevant information from a heavy volume of contents since the context information related to the content is not provided. To resolve these problems, this paper proposes an enhanced method of Web content adaptation for mobile devices. In our system, the process of Web content adaptation consists of 4 stages including block filtering, block title extraction, block content summarization, and personalization through learning. Learning is initiated when the user selects the full content menu from the content summary page. As a result of learning, personalization is realized by showing the information for the relevant block at the top of the content list. A series of experiments are performed to evaluate the content adaptation for a number of Web sites including online newspapers. The results of evaluation are satisfactory, both in block filtering accuracy and in user satisfaction by personalization.