• The Journal of Information Systems
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• v.28 no.3
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• pp.25-38
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• 2019

Purpose This study aims to empirically analyze which factors affect image and preference of YouTube beauty channels. Some practical and academic implications are presented through empirical research. Design/methodology/approach For this purpose, the six affecting factors were suggested on the basis of previous studies. We proposed image quality, user attitude, empirical value, economics, and awareness as independent variables and channel image and channel preference as dependent variables in order to investigate the causal relationships among the research variables. We surveyed 311 users who had experience in using YouTube Beauty channel and analyzed the data by using a statistical package. Findings This study showed that the channel image has a partial mediating effect between the affecting variables and the channel preference. The results provided some insights and information to increase the number of subscribers and site views. Several suggestions were carefully made.

• Journal of KIISE:Software and Applications
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• v.30 no.3_4
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• pp.316-325
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• 2003

A user preference prediction method using an exiting collaborative filtering technique has used the nearest-neighborhood method based on the user preference about items and has sought the user's similarity from the Pearson correlation coefficient. Therefore, it does not reflect any contents about items and also solve the problem of the sparsity. This study suggests the preference prediction system using the similarity weight granted Bayesian estimated value and the associative user clustering to complement problems of an exiting collaborative preference prediction method. This method suggested in this paper groups the user according to the Genre by using Association Rule Hypergraph Partitioning Algorithm and the new user is classified into one of these Genres by Naive Bayes classifier to slove the problem of sparsity in the collaborative filtering system. Besides, for get the similarity between users belonged to the classified genre and new users, this study allows the different estimated value to item which user vote through Naive Bayes learning. If the preference with estimated value is applied to the exiting Pearson correlation coefficient, it is able to promote the precision of the prediction by reducing the error of the prediction because of missing value. To estimate the performance of suggested method, the suggested method is compared with existing collaborative filtering techniques. As a result, the proposed method is efficient for improving the accuracy of prediction through solving problems of existing collaborative filtering techniques.

• Journal of information and communication convergence engineering
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• v.18 no.1
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• pp.55-60
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• 2020

As personalized customer services create a society that emphasizes the personality of an individual, the number of product reviews and quantity of user data generated by users on the internet in mobile shopping apps and sites are increasing. Such product review data are classified as unstructured data. Unstructured data have the potential to be transformed into information that companies and users can employ, using appropriate processing and analyses. However, existing systems do not reflect the detailed information they collect, such as user characteristics, purchase preference, or purchase priority while analyzing review data. Thus, it is challenging to provide customized recommendations for various users. Therefore, in this study, we have developed a product recommendation system that takes into account the user's priority, which they select, when searching for and purchasing a product. The recommendation system then displays the results to the user by processing and analyzing their preferences. Since the user's preference is considered, the user can obtain results that are more relevant.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.4 no.2
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• pp.174-190
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• 2010

In ubiquitous computing, invisible devices and software are connected to one another to provide convenient services to users [1][2]. Users hope to obtain a personalized service which is composed of customized devices among sharable devices in a ubiquitous smart space (which is called USS in this paper). However, the situations of each user are different and user preferences also are various. Although users request the same service in the same USS, the most suitable devices for composing the service are different for each user. For these user requirements, this paper proposes a device recommender system which infers and recommends customized devices for composing a user required service. The objective of this paper is the development of the systems for recommending devices through context-aware inference in peer-to-peer environments. For this goal, this paper considers the context and user preference. Also I implement a prototype system and test performance on the real ubiquitous mobile object (UMO).

• Archives of design research
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• v.15 no.4
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• pp.149-156
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• 2002

The success of a product is only possible on the basis of user preference for products and the user preference for products is greatly influenced by the design. Designers have to understand user preference and convert it into the combination of specified design attribute, and after that they should design products which have the image that they want to get. Then the product will be sure to be a hit. Therefore, on the point of view of design, it is necessary to find oui definitely the consumer preference frame : the relationship among design preference - design images - design attribute. This study 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 products offer is composed of. Therefore, in this study, after we developed the consumer response framework which is proposed by Bloch(1995) : distinct relationship model among preference - design image adjective - design factors, we analyzed the relationship among preference-design image adjective - design factors through the empirical researches. And then we give the way of design.

• Proceedings of the Korea Information Processing Society Conference
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• 2011.04a
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• pp.689-691
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• 2011

Due to the rapid development of Information and communication, the technology of multimedia presentation technology is evolving into the service that user can actively, realistically enjoy and play based on user's preference and taste not only for User's passive service. Especially, the industry related the realistic multimedia service that supports targeting Human emotion with the property of Human hearing is expected to be formed of the high value-added premium market. Audio technology is affected on human's emotion and the viewing environment around than video technology. Also the audio technology compared to video technology is a research part that appeals to human emotion and emphasize on psychological aspects. With this viewpoint, the development of intelligent and realistic audio technology needs highly specialty. In this study, "intelligent real-sound presentation technology" that support high quality and realistic audio and the "core technologies" that are composing of this will be introduced.

• Archives of design research
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• v.12 no.3
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• pp.105-114
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• 1999

As the microelectronics technology is developed, new types of smart intelligent products are being emerged. OSD user interface is one of the critical factor in this kind of product, especially brown goods and information devices, as it is responsible for imput and output function. OSD is being treated as accompaniment to hardware in spite of its importance, and therefore is developed from only simple and separate usability testing based on performance measurement. This study propose a usability evaluation method of OSD based on subjective preference to support existing usability testing. The purpose of this analysis is to make clear what is important factor and how its preference level is from the user's viewpoint. The various attributes of OSD are clarified from user's questionaire and interview, and orthogonal array is generated with specified factor levels. The prototypes are generated from rapid prototyping tool and tested in natural simulation environment. The preference data which collected in this usability testing is analyzed with conjoint analysis module. This usability evaluation is not the final stage in user interface design process but the early planned and circulated stage.

• Journal of Broadcast Engineering
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• v.7 no.2
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• pp.88-95
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• 2002

According to an evolution of digital broadcasting, it is possible that terrestrial and satellite broadcasting media provide multi-channel services. CATV and satellite media have been also extended to hundreds of channels. As the result of channel expanding, viewers came to select lots of channels. But it is difficult that they select the favorite channel among hundreds of channels. In this paper, we propose an efficient automatic method to recommend channels and programs on a viewer's preference in a multi-channel broadcasting receiver like a Set ToP Box(STB). The proposed algorithm selects channels based on the following method. It makes and saves user history data by using MPEG-7 MDS based on the program information a viewer had watched. It recommends programs similar to a viewer's preference based on user history data. It selects the channel in the recommended genre based on the viewer's channel preference. The experimental result shows that the proposed scheme is efficient to select the user preference channel.

• Journal of Environmental Science International
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• v.28 no.12
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• pp.1101-1110
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• 2019

This study investigated Post-Occupancy Evaluation (POE) of cherry blossom trails 'Cherry Road' in Daegu Catholic Univ. campus, at Gyeonsan-city, Korea. The evaluation focused on image preference and satisfaction of users i.e., students, using questionnaire surveys. A total 201 questionnaire samples were analyzed and most of the respondents were in the age group of 20. Frequency analysis was conducted on demographics, use behavior, reliability, and means. Factor analysis and multiple regression analysis were conducted for user satisfaction and image preference. Over 80% of visitors came with companions during daytime. The most common motives for use were strolling and walking, event and meeting, passing. For user satisfaction the mean scores were highest for landscape beauty (4.22), image improvement (4.14), campus image (4.08). Night lighting facility received the lowest score (3.32). Factor analysis concerning user satisfaction was categorized into environment-human behavior and physical factors. Multiple regression analysis showed that the overall satisfaction of user was significantly influenced by five independent variables: 'harmonious' (β=.214), 'night lighting facility' (β=.173), 'landscape beauty' (β=.208), 'lawn care' (β=.154), and 'walking trails' (β=.123). The mean scores of image variables were highest for 'beautiful' (5.81), 'bright' (5.67), and 'open' (5.64). The lowest scores was for 'quiet' (4.47). Exploratory factor analysis led to three factors being categorized: aesthetics, comforts, and simplicity. Result of multiple regression analysis indicated that the preference of space image was significantly influenced by five variables: 'bright' (β=.397), 'refreshing' (β=.211), 'cool' (β=.219), 'clean' (β=.182), and 'natural' (β=.-142). Hence, Cherry Road has a high level of user satisfaction and image evaluation, which is interpreted as having various cultural events and value for students on campus. To improve the satisfaction of Cherry Road in the future, it is necessary to secure night lighting, to manage trash cans, and to secure rest space.

• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.5
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• pp.526-529
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• 2003

Recommender systems or collaborative filtering are methods to identify potentially interesting or valuable items to a particular user Under the assumption that people with similar interest tend to like the similar types of items, these methods use a database on the preference of a set of users and predict the rating on the items that the user has not rated. Usually the preference of a particular user is liable to vary with time and this temporal variation may cause an inaccurate identification and prediction. In this paper we propose a method to adapt the temporal variation of the user preference in order to improve the predictive performance of a collaborative filtering algorithm. To be more specific, the correlation weight of the GroupLens system which is a general formulation of statistical collaborative filtering algorithm is modified to reflect only recent similarity between two user. The proposed method is evaluated for EachMovie dataset and shows much better prediction results compared with GrouPLens system.