• Archives of design research
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• v.19 no.1 s.63
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• pp.317-328
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• 2006

So far, the behaviors of Web users have been predicted or analyzed mostly by their demographic characteristics or by considering in which context they gain access to that. But now there is a question about whether those characteristics are the only factors to trigger their use of Web. If the answer is not affirmative, what types of additional factors could cause such an action and how they characterize it should be discussed. User profile information has been considered one of the crucial elements to define user characteristics in user-centered UI design sector, and in order to apply it to UI design, it's needed to meditate on the above-mentioned questions. In this study, it's first attempted to have a good understanding of the users of different media and to review existing user classification methods. Next, user classification variables and relevant scales were prepared to sort out users according to their type of using Web, and case study was conducted to identify the behavioral characteristics of users and classify them according to their behavioral features. Finally, the user profile features of individual user groups were figured out based on data that were gathered by making an experiment, and data mapping was fulfilled between the behavioral characteristics and user profile characteristics to find out what types of behaviors were caused by the characteristics of user profile. As a result, it's found that user characteristics could have an impact on not only their general information and relevant contexts but their attitude of using different media and personality type. There were some problems with the experimental design, but more accurate information on the relationship of user behaviors to user profile characteristics will be obtained if those problems are eliminated. As user behaviors could be predicted only by user profile characteristics, user classification is expected to make a contribution to enhancing the efficiency of UI design.

• Journal of KIISE:Software and Applications
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• v.31 no.8
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• pp.1062-1072
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• 2004

Collaborative filtering systems based on ｛user-document｝ matrix are effective in recommending web documents to user. But they have a shortcoming of decreasing the accuracy of recommendations by the first rater problem and the sparsity. This paper proposes the automatic preference rating method that generates user profile to solve the shortcoming. The profile in this paper is content-based collaborative user profile. The content-based collaborative user profile is generated by combining a content-based user profile with a collaborative user profile by mutual information method. Collaborative user profile is based on ｛user-document｝ matrix in collaborative filtering system, thus, content-based user profile is generated by relevance feedback in content-based filtering systems. After normalizing combined content-based collaborative user profiles, it automatically rates user preference by reflecting normalized profile in ｛user-document｝matrix of collaborative filtering systems. We evaluated our method on a large database of user ratings for web document and it was certified that was more efficient than existent methods.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.9
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• pp.1590-1595
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• 2006

This paper proposes a technique of user oriented document ranking using user refile to provide more satisfied results which reflect preference of specific users. User profile is constructed to represent his or her preference. User pfofile consists of 'term array' and 'preference vector' according to the interest field of one. And the User profile for a particular person is updated by 'user access', 'latent relaeon', 'User Profile' proposed in this paper. The latent structures of documents in same domain are analysed by singular value decomposition(SVD). Then, the rank of documents is determined by comparison of user profile with analyzed document on the basis of relevance.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.12 no.4
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• pp.270-276
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• 2012

With the increase in amount of data and information available on the web, there have been high demands on personalized information retrieval services to provide context-aware services for the web users. This paper proposes a novel dynamic multi-agent context-awareness user profile construction method based on ontology to incorporate concepts and properties to model the user profile. This method comprehensively considers the frequency and the specific of the concept in one document and its corresponding domain ontology to construct the user profile, based on which, a fuzzy c-means clustering method is adopted to cluster the user's interest domain, and a dynamic update policy is adopted to continuously consider the change of the users' interest. The simulation result shows that along with the gradual perfection of the our user profile, our proposed system is better than traditional semantic based retrieval system in terms of the Recall Ratio and Precision Ratio.

• The KIPS Transactions:PartC
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• v.15C no.1
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• pp.69-78
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• 2008

In this paper, we propose the effective exchange-method of a user profile for adapted contents services in the contents adaptation system. The profiles continuously exchange among the devices of contents adaption system for providing the adapted contents to a user. The profile should be also exchanged according to periodic, aperiodic, event, request and response. Consequently, a lot of network traffic occur and computing power require. Solving theses problem, the profile exchange research needs. we analyze creation information, exchange information, and exchange form of the profile information in contents adaptation system for effective profile exchange and define exchange procedure of the profile using the analyzed profile information. we also propose providing method of the profile configured information for decreasing profile processing time and user setting method for decreasing the number of profile transmission. As a result of performance evaluation, providing method of the profile configured information reduce 7% processing time and user setting method decrease the number of profile transmission are effective to exchange the profile.

• Journal of KIISE:Software and Applications
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• v.27 no.3
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• pp.248-256
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• 2000

This paper presents a personalized web agent that constructs user profile which consists of user preferences on the web and recommends his/her relevant information to the user. The personalized web agent consists of monitor agent, user profile construction agent, and user profile refinement agent. The monitor agent makes a user describe his/her preferences directly and it creates the database of preference document, finally performs several keyword extraction to increase the accuracy of the DB. The user profile construction agent transforms the extracted keywords into user profile that could be confirmed and edited by the user. and the refinement agent refines user profile by recursively learning and processing user feedback. In this paper, we describe the several keyword weighting and inductive learning techniques in detail. Finally, we describe the adaptive web retrieval and push agent that perform adaptive services to the user.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.1986-1989
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• 2003

This paper proposes the information retrieval improvement for the Web using the structure and hyperlinks of HTML documents along with user profile. The method bases on the rationale that terms appearing in different structure of documents may have different significance in identifying the documents. The method partitions the occurrence of terms in a document collection into six classes according to the tags in which particular terms occurred (such as Title, H1-H6 and Anchor). We use genetic algorithm to determine class importance values and expand user query. We also use this value in similarity computation and update user profile. Then a genetic algorithm is used again to select some terms from user profile to expand the original query. Lastly, the search engine uses the expanded query for searching and the results of the search engine are scored by similarity values between each result and the user profile. Vector space model is used and the weighting schemes of traditional information retrieval were extended to include class importance values. The tested results show that precision is up to 81.5%.

• Journal of KIISE:Software and Applications
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• v.31 no.4
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• pp.439-446
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• 2004

To design a user-adaptive web surfing system, we nay take the approach to divide the whole process into three phases; collecting user data, processing the data to construct and improve the user profile, and adapting to the user by applying the user profile. We have designed three software agents. Each privately works in each phase and they collaboratively support adaptive web surfing. They are IIA(Interactive Interface Agent), UPA(User Profile Agent), and ANA(Autonomous Navigation Agent). IIA provides the user interface, which collects data and performs mechanical navigation support. UPA processes the collected user data to build and update the user profile while user is web-surfing. ANA provides an autonomous navigation mode in which it automatically recommends web pages that are selected based on the user profile. The proposed approach and design method, through extensions and refinements, may be used to build a practical adaptive web surfing system.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• v.9 no.2
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• pp.615-618
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• 2005

This dissertation proposes a technique of user centered document ranking using User Profile to provide more satisfied results whitch feflect preference of speccific user. User Profile is comstructed to represent his reference of the user.

• International Journal of Computer Science & Network Security
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• v.22 no.11
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• pp.265-271
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• 2022

Social media is a form of communication based on the internet to share information through content and images. Their choice of profile images and type of image they post can be closely connected to their personality. The user posted images are designated as personality traits. The objective of this study is to predict five factor model personality dimensions from profile images by using deep learning and neural networks. Developed a deep learning framework-based neural network for personality prediction. The personality types of the Big Five Factor model can be quantified from user profile images. To measure the effectiveness, proposed two models using convolution Neural Networks to classify each personality of the user. Done performance analysis among two different models for efficiently predict personality traits from profile image. It was found that VGG-69 CNN models are best performing models for producing the classification accuracy of 91% to predict user personality traits.