• Journal of the Korea Society of Computer and Information
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• v.14 no.9
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• pp.85-94
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• 2009

It is becoming increasingly difficult for learners to find the lectures they are looking for. In turn, the ability to find the particular lecture sought by the learner in an accurate and prompt manner has become an important issue in e-Learning. To deal this issue, in this paper. we present a collaborative approach to provide personalized recommendations of online video lectures. The proposed approach first identifies candidated video lectures that will be of interest to a certain user. Partitioned collaborative filtering is employed as an approach in order to generate neighbor learners and predict learners'preferences for the lectures. Thereafter, Attribute-based filtering is employed to recommend a final list of video lectures that the target user will like the most.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.7 no.7
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• pp.847-856
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• 2017

In this paper, we propose a system to users which recommends suitable park using linguistic objects by rule-based inference engine which is made with Prolog. According to the function of city park, which provides positive elements to people such as social, psychological, environmental, and physical, Seoul city park is classified into 6 categories. The classified parks are recommended to users based on the rule based expert system. Rule-based object of park recommendation designs nine linguistic objects based on activity, multi-purposiveness, accessibility, and usage of time. This assigns allowed value accordingly. Generated rules by using these values are fired by user's preference, and infer recommended park. Information on preferences is obtained by way of dialogue, in which the user is asked questions about the three elements that are the criteria for choosing a park. As a result, through the park recommendation system, we intend to increase the user's satisfaction of using park and leisure activities.

• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.4
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• pp.443-449
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• 2014

Preferences of users on movies are observables of various factors that are related with user attributes and movie features. For movie recommendation, analysis methods for relation among users, movies, and preference patterns are mandatory. As a relational analysis tool, we focus on the Infinite Relational Model (IRM) which was introduced as a tool for multiple concept search. We show that IRM-based co-clustering on preference patterns and movie descriptors can be used as the first tool for movie recommender methods, especially content-based filtering approaches. By introducing a set of well-defined tag sets for movies and doing three-way co-clustering on a movie-rating matrix and a movie-tag matrix, we discovered various explainable relations among users and movies. We suggest various usages of IRM-based co-clustering, espcially, for incremental and dynamic recommender systems.

• International Journal of Highway Engineering
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• v.13 no.1
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• pp.197-206
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• 2011

The Korean government concentrates on supplying public transit facilities for the mobility handicapped. In other hands, increasing needs of transfer information when the mobility handicapped use transit facilities are substantial but not satisfactory as a whole. This study focuses on evaluating the applicability of developed K-path algorithm to provide user-customized route information that could make an active using of public transit while considering the mobility handicapped preferences. Developed algorithm reflects on requirements considering transfer attributes of the mobility handicapped. Trip attributes of the handicapped are addressed distinguished from handicapped types such as transfer walking time, transfer ratio, facility preferences and etc. This study examines the verification and application of the proposed algorithm that searches the least time K-paths by testing on actual subway networks in Seoul metropolitan areas. It is shown that the K-path algorithm is good enough to provide paths that meet the needs of the mobility handicapped and to be adoptable for the future expansion.

• Journal of the military operations research society of Korea
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• v.32 no.2
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• pp.21-39
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• 2006

This paper presents a mixed linear and integer goal programming (GP) model to aid in strategic planning and scheduling of guard soldiers. The proposed model is a general-purpose model, hence can be used to produce an optimal schedule with respect to any user-provided combination of guard post objectives and soldier preferences. We extensively test the usefulness of the model on a real-life dataset from a guard post in the ROK Army with using three objectives set by the guard post and three preferences provided by individual solders. Numerical results and analysis from these experiments show that the proposed guard scheduling model efficiently as well as effectively generates an optimal guard schedule and can also be used for an optimal revision of any existing schedule. In summary, these illustrate that the proposed model can be practically used for optimal planning and scheduling of guard soldiers in guard posts.

• Journal of Internet Computing and Services
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• v.10 no.4
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• pp.161-176
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• 2009

As the Internet has been growing, WWW(World Wide Web) based services were popularized and users using the service were increased excessively. To support these environments, a processing of many transactions became an essential consideration. There are many researches regarding the user profiling on web services to support those transactions effectively. Most of them are just for grasping the comsumer's preferences. However, a trend of recent web service is not limited what they are doing between consumers and providers; a consumer can become a provider and also a provider can become a consumer virtual open environments such as open market on the internet. For this reason, it is necessary to inspect a preference of consumers as well as providers one. In this paper, we proposed personal preference tree(PPT) reflecting the preferences for providers and implemented the module applying to the web service, and evaluated the applicability the personal preference module through a simulation.

• The Journal of the Korea Contents Association
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• v.11 no.5
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• pp.104-116
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• 2011

A recommender system is a personalized decision support tool to suggest suitable products in proper manners for the benefits of both suppliers and consumers, with the assumption of full understating of consumers' needs and preferences. However, a substantial number of studies have focused on making recommender systems more accurate and efficient. Whereas, there have been a few studies on consumers' needs and preferences under their own contexts to accept recommender systems. To this end, this study attempted to find out the impact of personal sentiments and contexts on the willingness to accept music recommender systems based on the simplified "Technology Acceptance Model" and some verified variables from the precedent studies. For the study, we conducted an empirical study using surveys and High-Order Structural Equation Model (SEM). The outcomes of the research was affirmative to the research hypothesis that the personal sentiments and contexts positively affect the acceptance of the music recommender systems.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.7
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• pp.970-977
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• 2018

Chinese mobile games enjoyed big success in Korean game market in 2017. On the surface, such success owes to their effort to strengthen the collaboration with Korean publishers and to relieve Chinese style in the game structure such as user interface. However, there seems to be other reasons for Korean mobile gamers to accept Chinese games more easily than before. In this paper, we analyze the preferences of Korean gamers playing chinese mobile games in 2017 by survey through many community sites. Among 201 subjects of our survey, 79% were males and most of them were under 20's. We found gender difference of game genre they played such that most young males played Moe-fied Fleet games but females played casual fashion collection games. Other than that, regardless of gender, Korean gamers preferred Chinese games' charging policy and management policy as well as the character illustrations that emphasized fantasy-style sexism (autome or ero-kawai).

• Journal of Intelligence and Information Systems
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• v.10 no.3
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• pp.133-146
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• 2004

By the development of the network technology, the types and amount of information that users keep in contact with have been dramatically increased. As a result, users are consuming a lot of time and energy to find needed information. On this, this article presents a new methodology that can efficiently manage their information within small cost by using content-based recommendation method and keyword affinity method. By using keyword affinity method, this methodology solves the content-based recommendation method's weak point that the performance is not good within the environment that the preferences of users are rapidly changing and new contents are created continuously and the accuracy level is low until the information of preferences are sufficiently gathered. This article carried out research on the personal e-mail environment where new information is frequently created and disappeared. Also this article assists folder recommendation for the efficient management of e-mail and verified the methodology mentioned above by an experiment to compare the performance of existing folder recommendation methods with the performance of this new method.

• 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.