• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.16 no.1
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• pp.78-89
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• 2017

This study derived the algorithm of a public transportation route search that adds safety and environmental costs according to user preference. As the means of an algorithm application and evaluation, Macro Simulation, VISUM was conducted for an analysis. The route using the subway, which is relatively low in safety and environment resistance value was preferred, and it was analyzed to select the safe and environmental route even though it detours. This study can be applicable when to verify the algorithm of route search considering safety and environment, and when introducing the algorithm of route search according to user preference in the smart-phone application in the future, it can provide users with very useful information by choosing a route as for safety and environment, and through this, the quality of user-friendly information provision can be promoted.

• Journal of Digital Convergence
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• v.15 no.1
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• pp.427-433
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• 2017

Mobile game users tend to value the opinions from their friends or SNS when making a decision on which game to play. This is because they are not satisfied with the information on suggestions provided by the conventional mobile game recommendation systems. In this research, we made a system that can reflect user preference directly by using Analytic Hierarchy Process(AHP). In the system, the hierarchy of AHP is composed of final goal(Level 1), evaluation basis(Level 2) and alternative(Level 3). And the system is made up of an input module, an AHP processing module, a recommendation module and database. Through comparison analysis with two conventional systems to test the performance of the system, we could find that the system got more higher satisfaction than the other systems.

• Journal of Science of Art and Design
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• v.8
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• pp.185-210
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• 2005

Closely connecting Well-Being culture with our traditional eating culture. we can predict next generation Kimchi refrigerator trend with this research. In the highly developing industrial society, design has been playing the central role in the managerial strategy of a company and has been one of the central agendas in determining the economical competence of a country, and has also been regarded as a means to acquire sustainable superior competence. Thus, these trends suggest that the aesthetic value of product has become more important than its technological function. In this study we reviewed theoretically esthetic factors influencing the preference and the evaluation of a product and made a list of eight esthetic factors based on various referential studies which include simplicity, balance, unity, rhythm, style, novelty, typicality, and proportion. Also on the point of view of design, it is necessary to find out definitely the consumer preference frame the relationship among design preference design image design attribute. Will give you guidelines on which designers can select and design some more objective and reliable design factors, finding out the relation of cause and effect by which they can know what kind of product designs their consumers like and how the popular image which that product offer is composed of. We investigated the esthetic factors affecting consumers preferences and the basis for evaluating a product. Aimed at providing materials for developing product design by presenting an ,esthetic guideline product design by presenting an esthetic guideline and to put these materials to practical use. Investigated other considered elements classified by manufactures and importance of esthetic factors and its influence on consumer tastes . All of these result, It could not conclude all of the adjective design image and design factors of every consumer, but through consumer reaction framework consumer are response and prefer the products which design image have. and then understand prefered design image are influenced to design factor's and could be apply to new design development.

• Database Research
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• v.34 no.3
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• pp.86-98
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• 2018

Point-of-Interest(POI) recommendation systems suggest the most interesting POIs to users considering the current location and time. With the rapid development of smartphones, internet-of-things, and location-based social networks, it has become feasible to accumulate huge amounts of user POI visits. Therefore, instant recommendation of interesting POIs at a given time is being widely recognized as important. To increase the performance of POI recommendation systems, several studies extracting users' POI sequential preference from POI check-in data, which is intended for implicit feedback, have been suggested. However, when constructing a model utilizing sequential preference, the model encounters possibility of data distortion because of a low number of observed check-ins which is attributed to intensified data sparsity. This paper suggests refinement of temporal intervals based on data confidence. When building a POI recommendation system using temporal intervals to model the POI sequential preference of users, our methodology reduces potential data distortion in the dataset and thus increases the performance of the recommendation system. We verify our model's effectiveness through the evaluation with the Foursquare and Gowalla dataset.

• Journal of The Korean Association of Information Education
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• v.24 no.6
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• pp.623-631
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• 2020

In the era of COVID-19 global pandemic, e-learning has become new standards and daily life in the name of 'new normal'. This study developed dialogue e-learning contents as opposed to monologue e-learning which is unidirectional and instructor centered and conducted qualitative user experience evaluation of dialogue e-learning contents. A total number of 20 adult students participated and were individually interviewed. Qualitative data analysis was performed. The findings include students' positive perceptions of dialogue e-learning contents such as empathy for various ideas and new format. With regard to personal preference, 55% of participants preferred dialogue e-learning contents because it enables them to focus and share real experiences. Meanwhile, in terms of learning effects, 60% participants selected monologue e-learning contents and mentioned adequate explanations of concepts and explicit information delivery. Based on the results, suggestions on the design and development of dialogue e-learning contents were presented.

• Archives of design research
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• v.12 no.1
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• pp.157-166
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• 1999

With the increase in use of the computer, the VCT syndrome has occured as a new socialJhea/health problem. ErgonomicaI design standards are a for the users to reduce stress and poor physical posture in the human body. In this study. we have suggested design dimensions recommended from previous studies. We also have reviewed users' preference dimenision, and analyzed differences between users' preference dimenision. and the previous design aiteria to verify physical appropriateness. We analyzed how each design dimension was reached and affected the tunan body by objective EMG evaluation. and subjective evaluation of physical discomfort and oorrIort. We have found that keyboard height is very important in a workstation. If the elbow's height is lower than the keyboard's height. it effedS the hand and wrist. If higher, it brings fatigue to the shoulder and neck. As a result of this experiment. we suggested that the height of a keyboard desk for Koreans be 660mm for the fixed type and 540-774mm for the adjustable type. Also other design reoommendations were suggested in the thesis. In ooncIusion, our research will be very important in the database because it provides adjustable ranges to fit user's body types in the various design flekIs.

• Proceedings of the Korean Information Science Society Conference
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• 2005.07a
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• pp.799-801
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• 2005

There have been many streaming servers that provide a large number of contents for a user's preference. General purpose streaming sewer makes use of a RTSP protocol for streaming controls such as message passing with client players. To date, there has been minimal research regarding streaming server's performance test tools. For measuring streaming server's performance, performance evaluation technique is needed and also achieved by RTSP based controls, a server's performance result and its miscellaneous test tools such the PseudoPlayer for pumping data to a specified port and the PseudoMonitor for gathering information. In this paper, We implement a test toolkit for evaluating a streaming server's performance and show the case of its application

• The Journal of the Korea Contents Association
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• v.16 no.4
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• pp.56-64
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• 2016

With the development of information technologies and the wide spread of smart devices, the number of users of social network services has increased exponentially. Studies that identify user preferences and recommend similar users in these social network services have been actively done. In this paper, we propose a new scheme to recommend social network friends with similar preferences through the moving pattern analysis of mobile users. The proposed scheme removes the meaningless trajectories via companions, short time trajectories, and repeated trajectories to determine the correct user preference. The proposed scheme calculates user similarity using the meaningful trajectories and recommends users with similar preferences as friends. It is shown through performance evaluation that the proposed scheme outperforms the existing schemes.

• Journal of Intelligence and Information Systems
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• v.22 no.3
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• pp.113-127
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• 2016

Among the techniques for recommendation, collaborative filtering (CF) is commonly recognized to be the most effective for implementing recommender systems. Until now, CF has been popularly studied and adopted in both academic and real-world applications. The basic idea of CF is to create recommendation results by finding correlations between users of a recommendation system. CF system compares users based on how similar they are, and recommend products to users by using other like-minded people's results of evaluation for each product. Thus, it is very important to compute evaluation similarities among users in CF because the recommendation quality depends on it. Typical CF uses user's explicit numeric ratings of items (i.e. quantitative information) when computing the similarities among users in CF. In other words, user's numeric ratings have been a sole source of user preference information in traditional CF. However, user ratings are unable to fully reflect user's actual preferences from time to time. According to several studies, users may more actively accommodate recommendation of reliable others when purchasing goods. Thus, trust relationship can be regarded as the informative source for identifying user's preference with accuracy. Under this background, we propose a new hybrid recommender system that fuses CF and social network analysis (SNA). The proposed system adopts the recommendation algorithm that additionally reflect the result analyzed by SNA. In detail, our proposed system is based on conventional memory-based CF, but it is designed to use both user's numeric ratings and trust relationship information between users when calculating user similarities. For this, our system creates and uses not only user-item rating matrix, but also user-to-user trust network. As the methods for calculating user similarity between users, we proposed two alternatives - one is algorithm calculating the degree of similarity between users by utilizing in-degree and out-degree centrality, which are the indices representing the central location in the social network. We named these approaches as 'Trust CF - All' and 'Trust CF - Conditional'. The other alternative is the algorithm reflecting a neighbor's score higher when a target user trusts the neighbor directly or indirectly. The direct or indirect trust relationship can be identified by searching trust network of users. In this study, we call this approach 'Trust CF - Search'. To validate the applicability of the proposed system, we used experimental data provided by LibRec that crawled from the entire FilmTrust website. It consists of ratings of movies and trust relationship network indicating who to trust between users. The experimental system was implemented using Microsoft Visual Basic for Applications (VBA) and UCINET 6. To examine the effectiveness of the proposed system, we compared the performance of our proposed method with one of conventional CF system. The performances of recommender system were evaluated by using average MAE (mean absolute error). The analysis results confirmed that in case of applying without conditions the in-degree centrality index of trusted network of users(i.e. Trust CF - All), the accuracy (MAE = 0.565134) was lower than conventional CF (MAE = 0.564966). And, in case of applying the in-degree centrality index only to the users with the out-degree centrality above a certain threshold value(i.e. Trust CF - Conditional), the proposed system improved the accuracy a little (MAE = 0.564909) compared to traditional CF. However, the algorithm searching based on the trusted network of users (i.e. Trust CF - Search) was found to show the best performance (MAE = 0.564846). And the result from paired samples t-test presented that Trust CF - Search outperformed conventional CF with 10% statistical significance level. Our study sheds a light on the application of user's trust relationship network information for facilitating electronic commerce by recommending proper items to users.

• The Journal of the Korea Contents Association
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• v.7 no.4
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• pp.66-73
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• 2007

Internet is a part of our common life and tremendous information is cumulated. In these trends, the personalization becomes a very important technology which could find exact information to present users. Previous personalized services use content based filtering which is able to recommend by analyzing the content and collaborative filtering which is able to recommend contents according to preference of users group. But, collaborative filtering needs the evaluation of some amount of data. Also, It cannot reflect all data of users because it recommends items based on data of some users who have similar inclination. Therefore, we need a new recommendation method which can recommend prefer items without preference data of users. In this paper, we proposed the apparel coordination system using personalized preference on the semantic web. This paper provides the results which this system can reduce the searching time and advance the customer satisfaction measurement according to user's feedback to system.