• Journal of the military operations research society of Korea
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• v.27 no.1
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• pp.114-127
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• 2001

To support cooperative work in various scenes by computer systems, functions of sharing personalization, observation and control are needed. Especially, in advanced cooperative work, people have various position so that sometimes one person needs to restrict behavior of another. In this paper, we describe representation of access restriction of hypermedia system, which is framework of CSCW(Computer-Supported Cooperative Works) system we developed and propose a way to go on working cooperatively with people, restricting others.

• 한국IT서비스학회:학술대회논문집
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• 2008.05a
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• pp.364-367
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• 2008

Cyber office is a personalized enterprise portal model for knowledge workers. The key services of cyber office are both personalization and emergent collaboration. Personalization service is provided by choosing service from service repository and then dropping it in the cyber office. Service repository contains various kinds of services supporting business activities. Cyber office provides blog service and profile service for knowledge workers to participate in knowledge based collaboration voluntarily.

• International Journal of Computer Science & Network Security
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• v.22 no.10
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• pp.369-373
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• 2022

In the decision-making domain, a document warehouse is designed to meet the analysis needs of users who may have a wide variety of analysis purposes. In this paper, we propose to integrate the preferences and interactions of users based on profiles to the concept of document warehouses. These profiles guarantee the integration of personalized documents and the collaborative recommendation of documents between different users sharing common interests.

• Journal of the Korean Society for Library and Information Science
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• v.39 no.3
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• pp.75-91
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• 2005

The popularization of the internet has produced an explosion in amount of the information. The importance of web personalization is being more and more increased. The personalization is realized by learning user's interest. User's interest is changing continuously and rapidly. We use user's profile to represent user's interest. User's profile is updated to reflect the change of user's interest. In this paper we present an adaptive learning algorithm that can be used to reflect user's interest that is changing with time. We propose the User's profile model. With this profile user's interest is learned based on user's feedback. This approach has applied to develop hybrid recommendation system.

• The Journal of the Korea Contents Association
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• v.11 no.9
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• pp.293-301
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• 2011

This study intends to deduce various purchase intention influential factor for mobile phone and thereby to clarify the causal relationship between these factors and purchase intention. As a result of the analysis of this study, we could know that first, when the impact about brand attitude on consumers is bigger, a positive affect is induced and the effects about personalization interface were also positive. Second, we could know that when the innovative tendency of consumers is stronger, they experience positive affects and the impact on personalization interface was also effective. However, it was clarified that when the innovative tendency is stronger, the impact on price gets smaller. Third, when the imitation tendency of consumers was higher, positive affects were induced, and when the interest in personalization interface of consumers was higher, positive affects were induced and the interest in price also gets higher. Fourth, we could check the influence of affect in the purchase intention of mobile phones.

• Journal of The Korean Association of Information Education
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• v.15 no.2
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• pp.219-226
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• 2011

Web personalization has focused on extracting web pages interesting to users, to help users searching wanted information efficiently on the web. One of the main methods to achieve this is by using queries, links and users' preferred words in the pages. In this study, we surveyed from the web users the features of pages that are considered important to themselves in selecting web pages. The survey results showed that the content of the pages is the most important. However, images and readability of the page are rated as high as the content for some users. Based on this result, we present a method for maintaining relative weights of major page features differently in the profile for each user, which is used for personalizing web search results. Performance of the proposed personalization method is analyzed to prove its superiority such that it yields as much as 1.5 times higher rate than the system utilizing both queries and preferred words and about 2.3 times higher rate than a generic search engine.

• Journal of the Korea Society for Simulation
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• v.15 no.4
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• pp.11-19
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• 2006

With the emergence of pervasive computing, personalization becomes an important issue to provide with users customized services, anywhere and anytime in their specific environment. Many researches have shown the possibilities of personalization by acquiring and processing sensor information around users. However, personalization remains still at its infancy, since most researches have failed to consider various contexts comprehensively besides sensor data, and just developed tailored services for a specific application domain. In this work, we propose a simulation-based CAS (context Aware Web Services) development methodology. Our methodology considers various contexts on users (eg. current location), web services (eg. response time), devices (eg. availability) and environment (eg. sensor data) all together by simulating them on the fly for personalized and adaptable services.

• The Journal of the Korea Contents Association
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• v.19 no.12
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• pp.376-388
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• 2019

One important online marketing practice to emerge in recent years is online behavioral advertising. Online behavioral advertising entails a range of issues, including the following: personal information collection and usage agreements (three conditions: no agreement, agreement, or agreement with a reward), consumers' levels of perceived personalization (low vs. high), and consumers' level of online privacy concerns (low vs. high). The effects of all these is what this study is designed to examine, as it evaluates online behavioral advertising. Study findings suggest that types of information collection and usage agreement play a pivotal role in the evaluation of online behavioral ads. Individuals who provided the informed consent form with the possible reward had a more favorable attitude toward the brand than individuals who provided the informed consent form without a possible reward. In terms of personalization, the level of perceived personalization of the advertising message impacted consumers' attitudes toward the online behavioral ad and toward the brand. Finally, online privacy concerns appear to impact consumers' attitudes toward the online behavioral ad and toward the brand. Theoretical and practical implications are also discussed.

• Journal of the HCI Society of Korea
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• v.4 no.1
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• pp.11-15
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• 2009

Social Network Service has been extremely popular these days and providing diverse features and functions to users. Social networking and interest sharing in between users are key factors of SNS and this circles back to draw more users to the service. Web is the first media to provide SNS and mobile is the next. The service based on mobile environmental uniqueness such as Location-based-service(LBS) is the key success factors to convert users to web SNS to mobile SNS. TV has also been a possible SSN market to draw users to share interests and participation. However TV has been always community electronics in family members and personalization to provide SNS has been barrier to overcome. In this study, we explorer ideas of key factors of personalization in TV environment and conducted a field study to define characteristics of TV personalization in terms of depth, method, style and structure. Research finds out that there are significant differences in these categories.

• The KIPS Transactions:PartD
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• v.11D no.3
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• pp.717-730
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• 2004

Recently. many researches on the personalization of a web-site have been actively made. The web personalization predicts the sets of the most interesting URLs for each user through data mining approaches such as clustering techniques. Most existing methods using clustering techniques represented the web transactions as bit vectors that represent whether users visit a certain WRL or not to cluster web transactions. The similarity of the web transactions was decided according to the match degree of bit vectors. However, since the existing methods consider only whether users visit a certain URL or not, users' interestingness on the URL is excluded from clustering web transactions. That is, it is possible that the web transactions with different visit proposes or inclinations are classified into the same group. In this paper. we propose an enhanced transaction modeling with interestingness weight to solve such problems and a new similarity measuring method that exploits the proposed transaction modeling. It is shown through performance evaluation that our similarity measuring method improves the accuracy of the web transaction clustering over the existing method.