• Journal of Fashion Business
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• v.26 no.3
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• pp.33-49
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• 2022

In the metaverse, it is important to embellish aesthetics of user with a character called 'Avatar', a virtual representation of the user. This study provides basic data related to the fashion trend of the metaverse by studying 'Zepeto', a representative Korean platform. For empirical research, Zepeto's "Best Items" section were investigated and analyzed in the first pre-survey. Based on this, the second and main survey was conducted using a questionnaire to investigate users' style-specific preferences and purchasing behaviors for virtual fashion, comparing style preferences between virtual and real, brand preferences, and purchasing behaviors of virtual fashion. The survey found that most users were teenage girls with a high preference for pastel-toned, feminine, and cute casual styles who had a much higher interest in brands bearing idol names than in real-world luxury brands. Many responded that they felt burdened by purchasing items that had to be purchased for cash. The same can be assumed to be the reason why they preferred a suit of items that were fully coordinated rather than individual items. These results seem to reflect characteristics of teenage girls who lack cash with a high preference for idols and feminine-cute casual styles. This study suggests considerations when creating virtual fashion items. By providing basic information, more effects and developments in creating virtual fashion items that reflect consumer preferences and reactions are expected in the future.

• Journal of Korea Game Society
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• v.9 no.6
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• pp.3-10
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• 2009

what is the elements letting users play games consistently other than the growth of the computer technology? What sort of elements can games give to control the balances in the various features of users? The subject of this study was set up for these questions. Examining the result of this study, the analysis of the types of individual characteristics as well as their preferences of playing games in the level of game design in the process of game development can be utilized as a fundamental data for the design of game contents based on user's propensity. Also it can provide the most effective method for controlling the balance of the game world. Moreover, it can be utilized to set up directions for updating the games based on user's propensity. If it can be counted for the potential users' preferences, the result of analysis of individual characteristic would act as good basic data for the balancing of game among various types of users.

• Korean Management Science Review
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• v.25 no.3
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• pp.135-155
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• 2008

This paper investigates the value of the expectation-confirmation model(ECM) of IT continuance in the internet shopping environments and attempts to expand the ECM with consideration of users diverse internet shopping factors and their preferences. Here, the extended ECM measures the users expectation-confirmation separately at the product, site and service levels, rather than treating it a single construct as measured in the original model. Further, AHP has been used for assessing the relative importance among the shopping factors, and the priority has been used for deriving weighted expectation-confirmation levels in the model. PLS analysis shows that there are significant differences in path coefficients between the ECM and a weighted ECM. This Indicates that users preferences among the diverse shopping factors need to be properly assessed to better understand the relationship between users expectation-confirmation and their continued usage behavior.

• Journal of Korea Multimedia Society
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• v.10 no.10
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• pp.1356-1366
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• 2007

Many personalized Services that provide users with adaptive information according to users' requirements and preferences have been researched and developed. However, existing approaches are difficult to share a user's information among heterogeneous services because these approaches manage users' preferences in a single system. In this paper, we propose a user preference sharing model based on the Semantic Web as a solution to resolve the problem. Our model enables user preferences to be described and shared over service-specific ontologies which are affected by the feature of each service. Our model is analyzed and evaluated with an implementation of the middleware that supports our model. Our approach has the advantage of providing more efficient personalized services than existing approaches because it can describe users' preferences centering around each service and share these information among heterogeneous personalized services.

• Journal of Internet Computing and Services
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• v.10 no.1
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• pp.83-91
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• 2009

The advertisement system on TV is categorized into full duplex TV and Internet Protocol Television (IPTV). For the full duplex TV or the IPTV, channel providers need a number of advertisements and pay enormous expenses for them. Therefore, this study proposes how to reduce unnecessary expenses based on users' preferences. The advertisement system based on users' preferences is designed to decrease unnecessary advertisements with less expense. To identify preferences in usual advertisements and animation advertisements is to reduce expenses due to the necessity of a number of advertisements. The proposed system was designed to provide full duplex advertisements for profits of advertisement industry and considered users' disposition based on preferences of broadcasting advertisements, through which users can have advertisement desired and providers can expect less advertisement expenses and more profits.

• Journal of Digital Convergence
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• v.11 no.3
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• pp.345-352
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• 2013

The demand and production for mobile music increases as the number of smart phone users increase. Thus, the standard of selection of a user's preferred music has gotten more diverse and complicated as the range of popular music has gotten wider. Research to find intelligent techniques to ingeniously recommend music on user preferences under mobile environment is actively being conducted. However, existing music recommendation systems do not consider and reflect users' preferences due to recommendations simply employing users' listening log. This paper suggests a personalized music-recommending system that well reflects users' preferences. Using AHP, it is possible to identify the musical preferences of every user. The user feedback based on the Bayesian network was applied to reflect continuous user's preference. The experiment was carried out among 12 participants (four groups with three persons for each group), resulting in a 87.5% satisfaction level.

• International Journal of Contents
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• v.17 no.1
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• pp.37-53
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• 2021

With the advent of the 5G era, smart phones and communications network technology have progressed, and mobile short video of people's life can be made, Of the new tools of communication, at present, China's social short video industry has shown rapid development, and the most representative of the short video app is Douyin (international version: Tiktok). Under the background of Uses and Gratifications Theory, this study discusse the relationship between Douyin users' preference degree, use motivation, use satisfaction and attention intention. This study divides the content of Douyin video into 10 categories, selects the form of an online questionnaire survey, uses SPSS software to conduct quantitative analysis of 202 questionnaires after screening, and finally draws the following conclusions: (1) The content preference degree of Douyin short video (the high group and low group) is different in users' use motivation, users' satisfaction degree and users' attention intention. ALL results are within the range of statistical significance.(2) Douyin users' video content preference degree has a positive impact on users' use motivation, users' satisfaction degree, and users' attention intention. (3) Douyin users' motivation has a positive impact on users' satisfaction and user' attention intention. (4) Douyin users' satisfaction degree has a positive impact on users' attention intention. Based on the research results, we suggest that Douyin platform pushes videos according to users' preferences. In addition, as the preference degree has an impact on users' motivation, satisfaction degree and attention intention of using the platform, it is important that the platform's focus should to pay attention to the preference degree of users. Collecting users' preferences at the early stage of users' entering the platform is a good way to learn from, and doing a good job of big data collection and management in the later operation.

• Journal of Korea Multimedia Society
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• v.18 no.9
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• pp.1068-1082
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• 2015

Due to the rapid advancement of the mobile technology, smart phones have been widely used in the current society. This lead to an easier way to retrieve video contents using web and mobile services. However, it is not a trivial problem to retrieve particular video contents based on users' specific preferences. The current movie recommendation system is based on the users' preference information. However, this system does not consider any emotional means or perspectives in each movie, which results in the dissatisfaction of user's emotional requirements. In order to address users' preferences and emotional requirements, this research proposes a movie recommendation technology to represent a movie's emotion and its associations. The proposed approach contains the development of emotion ontology by representing the relationship between the emotion and the concepts which cause emotional effects. Based on the current movie metadata ontology, this research also developed movie-emotion ontology based on the representation of the metadata related to the emotion. The proposed movie recommendation method recommends the movie by using movie-emotion ontology based on the emotion knowledge. Using this proposed approach, the user will be able to get the list of movies based on their preferences and emotional requirements.

• Journal of Information Processing Systems
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• v.9 no.3
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• pp.435-460
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• 2013

Recommender systems are popular applications that help users to identify items that they could be interested in. A recent research area on recommender systems focuses on detecting several kinds of inconsistencies associated with the user preferences. However, the majority of previous works in this direction just process anomalies that are intentionally introduced by users. In contrast, this paper is centered on finding the way to remove non-malicious anomalies, specifically in collaborative filtering systems. A review of the state-of-the-art in this field shows that no previous work has been carried out for recommendation systems and general data mining scenarios, to exactly perform this preprocessing task. More specifically, in this paper we propose a method that is based on the extraction of knowledge from the dataset in the form of rating regularities (similar to frequent patterns), and their use in order to remove anomalous preferences provided by users. Experiments show that the application of the procedure as a preprocessing step improves the performance of a data-mining task associated with the recommendation and also effectively detects the anomalous preferences.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.2
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• pp.744-768
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• 2016

As various pieces of information can be provided through the web, schemes that provide search results optimized for individual users are required in consideration of user preference. Since the existing social search schemes use users' profiles, the accuracy of the search deteriorates. They also decrease the reliability of a search result because they do not consider a search time. Therefore, a new social search scheme that considers temporal information as well as popularities and user preferences is required. In this paper, we propose a new mobile social search scheme considering popularities and user preferences based on temporal information. Popularity is calculated by collecting the visiting records of users, while user preference is generated by the actual visiting information among the search results. In order to extract meaningful information from the search target objects that have multiple attributes, a skyline processing method is used, and rank is given to the search results by combining the user preference and the popularity with the skyline processing result. To show the superiority of the proposed scheme, we conduct performance evaluations of the existing scheme and the proposed scheme.