• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.6
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• pp.3055-3073
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• 2019

In this paper, a new user identification method is presented using real environmental human-computer-interaction (HCI) behavior data to improve method usability. User behavior data in this paper are collected continuously without setting experimental scenes such as text length, action number, etc. To illustrate the characteristics of real environmental HCI data, probability density distribution and performance of keyboard and mouse data are analyzed through the random sampling method and Support Vector Machine(SVM) algorithm. Based on the analysis of HCI behavior data in a real environment, the Multiple Kernel Learning (MKL) method is first used for user HCI behavior identification due to the heterogeneity of keyboard and mouse data. All possible kernel methods are compared to determine the MKL algorithm's parameters to ensure the robustness of the algorithm. Data analysis results show that keyboard data have a narrower range of probability density distribution than mouse data. Keyboard data have better performance with a 1-min time window, while that of mouse data is achieved with a 10-min time window. Finally, experiments using the MKL algorithm with three global polynomial kernels and ten local Gaussian kernels achieve a user identification accuracy of 83.03% in a real environmental HCI dataset, which demonstrates that the proposed method achieves an encouraging performance.

• Journal of KIISE:Computing Practices and Letters
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• v.16 no.10
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• pp.985-989
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• 2010

The near future smart ubiquitous computing oriented system will have to provide the optimal (right) service through interactions between users and the system. To provide the right services what the user needs, we should choose the user-centered development for reflecting the user needs, but not the developer-centered development. To do this, we proposed User Behavior Analysis Based Needs Extraction Method [1]. In this paper, we propose Object Extraction and Modeling Method from the user requirements with Fillmore's Case Grammar.

• Journal of Korea Society of Digital Industry and Information Management
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• v.12 no.1
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• pp.119-129
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• 2016

Rapid changes to the paradigm of smart media have created a communication environment through merging with various media such as IoT technology, which is internet of things. Among them, user' need for a start home, which is one of people living conditions, has been growing and values of the communication environment in a living space using a smart mirror have been growing. However, studies on figuring out behavior and analyzing needs of family members who actually use the living space are insufficient. This study is to draw the service system of the home smart mirror by analysis of behavior and needs of users of the living space. For a research method for analysis of behavior of family members, I wrote two kinds of user's experience maps, which are frequency of use of a quantitative space of a living space and space's important value scales by persona study and depth interview. Through this, applied spaces of the home smart mirror (living room, bathroom, powder room, dress room, porch, kitchen, room) and the types of user needs (type of providing information, entertainment type, control type, service type) have been drawn and statistical analysis methodology has been utilized for a research of user preferences in regard to correlation between living spaces and types of user needs based on a survey. As a result of analysis of ages and gender, types of user needs by space have been drawn and the service system of the home smart mirror has been drawn. It would be utilized as a basic material for various contents development and design using the smart mirror in the future.

• International Journal of Internet, Broadcasting and Communication
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• v.11 no.3
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• pp.95-105
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• 2019

A great deal needs to be learned about why and how users participate and consume information on various online sites. The design of socio-technical systems especially for promoting engagement in terms of maximum user participation is both a theoretical and real-world challenge that researchers strive to understand. At present, most of the research on the motives of Internet video users' behavior focuses on the user's "viewing motivation" and "sharing motivation", and lacks the analysis of the factors affecting users' "subscription motivation". This study will attempt to compensate for this gap. Based on the YouTube platform, we take Chinese video users as the research object and uses the "Censydiam user motivation analysis model" to make assumptions about user subscription motivation from the two levels of social needs and personal needs, using regression analysis. Validate the hypothesis and get the influencing factors that may be available in the user's subscription motivation based on the assumptions. Built on survey data from 215 respondents, the study found that Enjoyment, Vitality, Power, and Conviviality are four factors that influence user motivation.

• Journal of Information Technology Services
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• v.16 no.1
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• pp.119-138
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• 2017

Mobile easy payment is a term for using mobile devices to facilitate financial transactions. Mobile easy payment is a quickly emerging practice, supported by the dramatic increase of mobile phone or device users around the world. The purpose of this study is to classify user's group of the mobile easy payment service by their lifestyle based on I-VALS type and to investigate differences in usage behavior among the user groups. For empirical study, a questionnaire survey was carried out with users of mobile easy payment services using the Internet from June 7 to 10 in 2016. We conducted factor analysis for I-VALS, usage factors, satisfaction factors. We also performed cluster analysis for group classification, and analysis of variance, cross analysis to compare the different usage behavior among the user groups. In analysis results, first three cluster appeared including "ICT task-oriented group", ICT positive and socialities oriented group", "ICT leader-oriented group". Second, it was found significant differences among the clusters about factor of "easy of use", "securities", "universality", "satisfaction". Third, we showed that there were significant differences in time first used the service. Based on these findings, we provide practical implications. Results of our study can be expected to provide the basis of differentiated marketing strategy of mobile easy payment services.

• Journal of the Korea Society of Computer and Information
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• v.20 no.1
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• pp.29-37
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• 2015

Nowadays, the user-centric application based web 2.0 has replaced the web 1.0. The users gain and provide information by interactive network applications. As a result, traditional approaches that only extract and analyze users' local document operating behavior and network browsing behavior to build the users' preference profile cannot fully reflect their interests. Therefore this paper proposed a preference analysis and indicating approach based on the users' communication information from MicroBlog, such as reading, forwarding and @ behavior, and using the improved PersonalRank method to analyze the importance of a user to other users in the network and based on the users' communication behavior to update the weight of the items in the user preference. Simulation result shows that our proposed method outperforms the ontology model, TREC model, and the category model in terms of 11SPR value.

• Journal of Information Science Theory and Practice
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• v.8 no.1
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• pp.45-55
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• 2020

The purpose of this work was to study library website users' actions by tracking their behavior, determining popular content, and identifying browsing patterns and subsequent improvement of access to popular content. The study of behavior models and the use of web analytics has led to the emergence of solutions that improve the usability and functionality of the State Public Scientific-Technological Library of the Siberian Branch of the Russian Academy of Sciences (SPSTL SB RAS) website. These are: identifying user tasks as they are developed, conducting user testing to better understand the event. tracking data and collecting additional data to verify the effectiveness of the changes made. Examining data on the duration of the session and the number of visits will help determine the goals of user visits and develop new recommendations. Usability analysis and testing will make it possible to compare the data obtained using web analytics and the perception of the library site by the users themselves. Recommendations are offered to libraries on the use of data on the real behavior of the target audience of the library website to improve access to library resources and services, increase their relevance and improve information services.

• 한국HCI학회:학술대회논문집
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• 2008.02b
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• pp.460-464
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• 2008

To improve and optimize user interfaces of the system, the accurate understanding of users' behavior is an essential prerequisite. Direct questions depend on user' s ambiguous memory and usability tests depend on the researchers' intention instead of users'. Furthermore, they do not provide with natural context of use. In this paper we described the work which examined users' behavior through log analysis in their own environment. 50 users were recruited by consumer segmentation and they were downloaded logging-software in their mobile phone. After two weeks, logged data were gathered and analyzed. The complementary methods such as a user diary and an interview were conducted. The result of the analysis showed the frequency of menu and key access, used time, data storage and several usage patterns. Also, it was found that users could be segmented into new groups by their usage patterns. The improvement of the mobile phone user interface was proposed based on the result of this study.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.22 no.2
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• pp.225-238
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• 2012

Among the various security threats in online games, the use of game bots is the most serious problem. In this paper, we propose a framework for user behavior analysis for bot detection in online games. Specifically, we focus on party play that reflects the social activities of gamers: In a Massively Multi-user Online Role Playing Game (MMORPG), party play log includes a distinguished information that can classify game users under normal-user and abnormal-user. That is because the bot users' main activities target on the acquisition of cyber assets. Through a statistical analysis of user behaviors in game activity logs, we establish the threshold levels of the activities that allow us to identify game bots. Also, we build a knowledge base of detection rules based on this statistical analysis. We apply these rule reasoner to the sixth most popular online game in the world. As a result, we can detect game bot users with a high accuracy rate of 95.92%.

• Journal of the HCI Society of Korea
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• v.4 no.2
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• pp.1-8
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• 2009

User-centered design aims to develop device naturally without the interference of user' s conscious and unconscious behavior. Accordingly, designers need to understand their user's requirements, observe user behavior and interaction in the real environment. However, existing observations suggested a vast range of analysis system and observation techniques which are often ambiguous to the designers. Therefore, this research is aimed to propose an observation system for collecting data from user's behavior. In order to do so, we define the components and behavior level within the context of driving.