• Journal of the Korean Society for information Management
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• v.17 no.3
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• pp.93-108
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• 2000

The purpose of this study is to analyze user needs of university libraries for design of user-centered information retrieval system. To do this, 6 kinds of basic system design factors necessary for user-centered information retrieval system design of the university libraries are used. The user needs which should be considered for system design are examined and analyzed through the questionnaires made by user-centered approach according to the design factors.

• Proceedings of the Korean Information Science Society Conference
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• 2004.04b
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• pp.7-9
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• 2004

With the development of e-commerce and information access, recommender systems have become a popular technique to prune large information spaces so that users are directed toward those items that best meet their needs and preferences. In this paper, clustering technique is applied in the collaborative recommender framework to consider semantic contents available from the user profiles. We also suggest methods to construct user profiles from rating information and attributes of items to accommodate user preferences. Further, we show that the correct application of the semantic content information obtained from user profiles does enhance the effectiveness of collaborative recommendation.

• Proceedings of the Korean Information Science Society Conference
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• 2004.10c
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• pp.385-387
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• 2004

최근 지하철이나 버스 등의 교통수단을 이용하기 위해 비접촉식 카드를 이용한 IC카드 Reader가 많이 사용되고 있다. 이러한 IC카드 Header는 ID를 인증하기 위한 UserID를 Reader내부에 저장하고 있으며 UserID가 바뀔 때 마다 상위 시스템(서버)으로부터 전송받아 최신의 UserID를 가지고 있어야 한다. 교통시스템에서 사용되는 통신매체는 환경의 특수함으로 인해 낮은 전송속도를 이용하여 통신하고 있고, 그로인해 UserID를 전송할 때 시간이 많이 소모된다. UserID를 전송하는 동안에는 IC카드 Reader의 사용이 제한되기 때문에 UserID는 IC카드 Reader가 사용되지 않는 시간에 전송하고 있다. 많은 양의 UserID를 IC카드 Reader에 빠르게 전송하기 위해 UserID의 특성을 이용한 엔코딩(encoding) 기법을 적용 하여 UserID의 크기를 줄여 전송함으로써 전송시간을 줄이는 방법을 제안하였다.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.14 no.2
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• pp.122-129
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• 2014

Effective information gathering and retrieval of the most relevant web documents on the topic of interest is difficult due to the large amount of information that exists in various formats. Current information gathering and retrieval techniques are unable to exploit semantic knowledge within documents in the "big data" environment; therefore, they cannot provide precise answers to specific questions. Existing commercial big data analytic platforms are restricted to a single data type; moreover, different big data analytic platforms are effective at processing different data types. Therefore, the development of a common big data platform that is suitable for efficiently processing various data types is needed. Furthermore, users often possess more than one intelligent device. It is therefore important to find an efficient preference profile construction approach to record the user context and personalized applications. In this way, user needs can be tailored according to the user's dynamic interests by tracking all devices owned by the user.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.931-934
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• 2005

This paper describes an adaptive recommendation system that provides real-time personalized trading advice to the investors based on their profiles and trading information environment. A proposed system integrates Stochastic technical analysis and artificial neural network that incorporates an adaptive user modeling. The user model is constructed and updated based on initial user profile and recorded user interactions with the system. The information presented to each individual user is also tailor-made to fit the user's behavior and preference. A system prototype was implemented in JAVA. Experiments used to evaluate the system's performance were done on both human subjects and synthetic users. The results show our proposed system is able to rapidly learn to provide appropriate advice to different types of users.

• Journal of the Korea Society of Computer and Information
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• v.21 no.7
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• pp.9-16
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• 2016

Collaborative filtering(CF) uses the purchase or item rating history of other users, but does not need additional properties or attributes of users and items. Hence CF is known th be the most successful recommendation technology. But conventional CF approach has some significant weakness, such as the new user problem. In this paper, we propose a approach using word embedding with skip-gram for learning distributed item representations. In particular, we show that this approach can be used to capture precise item for solving the "new user problem." The proposed approach has been tested on the Movielens databases. We compare the performance of the user based CF, item based CF and our approach by observing the change of recommendation results according to the different number of item rating information. The experimental results shows the improvement in our approach in measuring the precision applied to new user problem situations.

• Proceedings of the Korea Contents Association Conference
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• 2008.05a
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• pp.614-621
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• 2008

With the proliferation of information and diversity of user environment, the filtering of information for providing suitable contents to user becomes more important. This paper represents the platform for user-context based contents service taking account of user's various environments. To make information more useful, web server devote their effort to select proper contents and reconstruct them according to the user preferences and environment condition. Finally we implement contents provider presenting personalized information for the prehistoric age in virtual museum on the web.

• Journal of Korea Society of Industrial Information Systems
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• v.14 no.2
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• pp.1-7
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• 2009

This paper proposes a context gathering system that obtains user's environment information from sensor and generates user's context profile. To design the system, we classify context and design context model based on traditional context-aware computing. The proposed system supports contexts adaptation service by gathering user's environment characteristics and biological characteristics and generating user profile.

• Asia pacific journal of information systems
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• v.14 no.1
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• pp.101-123
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• 2004

User satisfaction has been widely used by information system(IS) researchers as the most appropriate surrogate variable for the systems success since Bailey and Pearson(1983) provided their user satisfaction measurement. Because user satisfaction is a perceived performance measure by users, not a real or objective measure for systems success, however, perceived user satisfaction by users may not be exactly identical with the real systems performance. In this regard, if the user's ultimately perceived satisfaction is different from the real systems performance, we need to investigate why these two measures are different and which factors may cause the difference. From the perspective of disconfirmation of user expecations, this study examined why user satisfaction and real systems performance may not be identical each other. Expectaion-disconfirmation theory which has had a central role in marketing in explaining the effects of expectation and disconfirmation on consumer satisfaction was similarly adopted in this study to explain the role of expectation and disconfirmation in user satisfaction in the IS environment. Based on the expectation-disconfirmation theory, the current study developed a research model to examine the effects of expectation, system performance, and disconfirmation on user satisfaction in particular. Six research hypotheses derived from the research model were empirically tested using the partial least squares(PLS) method. The results of the statistical analysis indicate that the effects of system performance and disconfirmation were fairly strong on user satisfaction, while the user's expectation has shown insignificant influences on user satisfaction.

• Journal of Information Science Theory and Practice
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• v.2 no.3
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• pp.6-17
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• 2014

The purpose of this study is to examine to what extent the context in which people interact with online information affects people's credibility perceptions. In this study, credibility assessment is defined as perceptions of credibility relying on individuals' expertise and knowledge. Context has been characterized with respect to three aspects: Context as user goals and intentions, context as topicality of information, and context as information activities. The data were collected from two empirical studies. Study 1 was a diary study in which 333 residents in Michigan, U.S.A. submitted 2,471 diary entries to report their trust perceptions associated with ten different user goals and nine different intentions. Study 2 was a lab-based study in which 64 subjects participated in performing four search tasks in two different information activity conditions - information search or content creation. There are three major findings of this study: (1) Score-based trust perceptions provided limited views of people's credibility perceptions because respondents tended to score trust ratings consistently high across various user goals and intentions; (2) The topicality of information mattered more when study subjects assessed the credibility of user generated content (UGC) than with traditional media content (TMC); (3) Subjects of this study exerted more effort into making credibility judgments when they engaged in searching activities than in content creation. These findings indicate that credibility assessment can or should be seen as a process-oriented notion incorporating various information use contexts beyond simple rating-based evaluation. The theoretical contributions for information scientists and practical implications for web designers are also discussed.