• Journal of KIISE:Software and Applications
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• v.32 no.11
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• pp.1111-1120
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• 2005

In collaborative filtering systems most users do not rate preferences; so User-Item matrix shows great sparsity because it has missing values for items not rated by users. Generally, the systems predict the preferences of an active user based on the preferences of a group of users. However, default voting methods predict all missing values for all users in User-Item matrix. One of the most common methods predicting default voting values tried two different approaches using the average rating for a user or using the average rating for an item. However, there is a problem that they did not consider the characteristics of items, users, and the distribution of data set. We replace the missing values in the User-Item matrix by the default noting method using user coefficient of variance. We select the threshold of user coefficient of variance by using equations automatically and determine when to shift between the user averages and item averages according to the threshold. However, there are not always regular relations between the averages and the thresholds of user coefficient of variances in datasets. It is caused that the distribution information of user coefficient of variances in datasets affects the threshold of user coefficient of variance as well as their average. We decide the threshold of user coefficient of valiance by combining them. We evaluate our method on MovieLens dataset of user ratings for movies and show that it outperforms previously default voting methods.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 2008.10a
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• pp.5-8
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• 2008

This study proposed the Knowledge Base Building method reflecting the user's preferences based on the fuzzy set theory to develop information contents which support pedestrian's navigation. This research evaluated subject's preferences on the commercial spaces set to the hypothetical destination. Also it surveyed the causal relationship between the visual characteristics and the emotional characteristics to propose the methods of Navigation Knowledge Base (NKB). The NKB was composed by three elements; 1.the correlation model between emotional characteristics, 2.the causal relationship between visual characteristics and emotional characteristics, 3.the transformation model between visual characteristics and the physical characteristics.

• Journal of the Semiconductor & Display Technology
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• v.7 no.4
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• pp.13-18
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• 2008

This paper explores a content-based image retrieval framework with relevance feedback based on genetic algorithm (GA). This framework adopts GA to learn the user preferences using the similarity functions defined for all available descriptors. The objective of the GA-based learning methods is to learn the user preferences using the similarity functions and to find a descriptor combination function that best represents the user perception. Experiments were performed to validate the proposed frameworks. The experiments employed the natural image databases and color and texture descriptors to represent the content of database images. The proposed frameworks were compared with the other two relevance feedback methods regarding effectiveness in image retrieval tasks. Experiment results demonstrate the superiority of the proposed method.

• Journal of The Institute of Information and Telecommunication Facilities Engineering
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• v.4 no.1
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• pp.23-29
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• 2005

This study is about designing mobile phone display in consideration of elder people's preferences by reason of their optical weakness. The research is closely connected with designing user-friendly interface by considering user characteristics. The criteria for first experiment are font sizes, font types, line spacing and background colors. With the experiment result, relative importance of each attribute and subjective preference are investigated by conjoint analysis. Secondly, an optimal display design for elder people is presented by response surface method on the basis of the result of conjoint analysis, other statistical analyses, and user interviews.

• Journal of information and communication convergence engineering
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• v.22 no.1
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• pp.7-13
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• 2024

User preferences and ratings may be anticipated by recommendation systems, which are widely used in social networking, online shopping, healthcare, and even energy efficiency. Constructing trustworthy recommender systems for various applications, requires the analysis and mining of vast quantities of user data, including demographics. This study focuses on holding elections with vague voter and candidate preferences. Collaborative user ratings are used by filtering algorithms to provide suggestions. To avoid information overload, consumers are directed towards items that they are more likely to prefer based on the profile data used by recommender systems. Better interactions between governments, residents, and businesses may result from studies on recommender systems that facilitate the use of e-government services. To broaden people's access to the democratic process, the concept of "e-democracy" applies new media technologies. This study provides a framework for an electronic voting advisory system that uses machine learning.

• 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.

• Journal of the Korea Society of Computer and Information
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• v.21 no.3
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• pp.73-81
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• 2016

With the increasing popularity of social network services (SNSs), there have been many attempts to analyze the users of SNSs. By doing so, the characteristics and preferences of the users can be understood, which can help companies provide personalized information and services that they need or are relevant for them. This study aimed to analyze the usage behavior of Korean Twitter users from various perspectives to deepen the understanding of it. For this research goal, an online survey was conducted for the users of Twitter and the data about their actual usage were collected using the open API of Twitter. Factor analysis of the data revealed five factors that explain about 69.3% of the usage variables. It was also investigated how the factors are related to gender, age, and brand preferences. The results showed that the usage behavior of Twitter is largely affected by age (p<0.001), and also by gender through an interaction effect (p<0.05). Also, the factors showed significant statistical correlations with the brand preferences of the users.

• Journal of Ginseng Research
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• v.13 no.1
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• pp.136-141
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• 1989

The purpose of this survey was to understand the attitudes, consumption patterns and preferences of domestic consumers relating to ginseng products in general. This study involved 1,305 people (420 agents, 742 ginseng farmer, 143 staff of Korea Tobacco and Ginseng Co.). The results obtained are summerized as follows. 1, Preferences for ginseng were very high for the majority of the respondents. 2. Most (80%) of the respondents had experience in taking ginseng. They expected ginseng to have efficacy as remidy for the hang-over syndrome, gastronil troubles and high blood pressure, in that order. 3. The patron of ginseng were, for the most part, men in the prime of manhood and old age. The favored products and the preferences regarding the organic condition of the ginseng products differed by sex and age. 4 To sum up the results of this study, we conclude that moderate priced ginseng products, not only easy to use and carry but also attractive to age and sex, should be developed without losing the efficacious components and characteristics.

• The Journal of the Korea Contents Association
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• v.9 no.12
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• pp.900-911
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• 2009

This paper analyzes user preferences on the basic structure of digital textbook contents based on the PDF and HTML formats. This was conducted by analysing the data from an online survey on user preferences for the representative structures of the PDF- and HTML-based digital textbook contents that are currently used on the Web. Results show that in the PDF format, the structure with TOC(Table Of Contents) links on the left screen and the main content on the right was most preferred by 82% of the respondents. In terms of the viewing method, the one that presents one page of the textbook fitted to the width of the computer screen in a single-page view was regarded as the best. Similarly, in the HTML format, the structure with TOC links on the left frame and the main content on the right using 2-frames was revealed as the most preferred by 84% of the respondents. However, the structures of the PDF- and HTML-based digital textbook contents employed by most existing Web sites go against the users' preferences. Accordingly, for digital textbook development in the future, user preferences must be considered to allow students to read the contents more easily and conveniently.

• The Journal of the Korea Contents Association
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• v.10 no.6
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• pp.18-26
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• 2010

Many personalized services that provide users with adaptive information according to users' preferences have been researched and developed. Push services are especially expected to be more economic impact because push services satisfy user's potential needs even if the user does not require anything. In this paper, we propose Semantic Web approach in order to enhance the performance of push services. Our approach provides infrastructure to recommend contents based on semantic association by enabling information of contents and user preferences to be described on service-specific ontologies that reflect features of each service. In addition, our approach can recommend users with adaptive information based on information represented in our description model. Our approach enables information of contents and user preferences to be described with rich expressiveness, and it provides semantic interoperability.