• Journal of the Ergonomics Society of Korea
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• v.33 no.3
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• pp.175-189
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• 2014

Objective: The goal of this research is to develop new user behavior model using user motion analysis with microscopic perspective for attracting user's participation in interactive space. Background: The interactive space is 'human's place', which is made up of complex elements of digital virtual space and traditional analog and physical environment based on human-computer interaction system. Human behavior has changed in it at the same time. If the user couldn't make participation in interaction, the purpose of the system is not met, which reduces its effect. Therefore, we need to focus on interactive space that is potential future direction from a new point of view. Method: For this research, we would discuss and study fields of interactive space; (1) finding definition of interactive space and studying background of theory about it. (2) providing base of user behavior model with study of user's context that is to be user information and motion. (3) examining user motion, classify basic motion type and making user participation behavior model in phases. Results: Through this process, user's basic twenty motions which are systematized are taken as a standard for analysis of interaction process and participation in interactive space. Then, 'NK-$I^5$ (I Five)' model is developed for user participation types in interactive space. There are five phases of user participation behavior: Imperception, Interest, Involvement, Immersion, and Influence. In this analysis, three indicators which are time, motion types, and user relationship are found to be related to participation. Conclusion: The capabilities and limitation of this research is discussed to attract user participation. This paper focuses especially on contribution of design to lead user's participation in interactive system and expectation to help adapt to user centered design of various interactive space with new aspect of user behavior research. Application: The results of the 'NK-$I^5$ (I Five)' model might help to realize successful interactive space based on user centered design.

• Design Convergence Study
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• v.16 no.5
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• pp.203-217
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• 2017

This study proposes and demonstrates the value of user - centered design methodology based on linguistic analysis. The results of the proposed user behavioural analysis framework suggested that the syntactic structure between the sentence structure and its components could be a logical basis for explaining the user's situation and behavior. Based on this, the definitions and classifications of user interactions and user contexts were conducted in a microscopically context. User behavior has also been established to identify pattern structures of purposeful nature and constitutes a user behavior sequence that prioritizes them. Next, the User Experience Analysis Framework was derived by defining the relationship between User Behavior and User Behavior and User Context and User Context. To verify the framework of the framework, a professional assessment was conducted to conduct a review of the user's experience and conduct a study of the framework of the framework and conduct of the framework of the framework of the framework and practical utility of the framework. Through this, it was possible to identify the value of the qualitative and quantitative framework of the framework and the future direction of development.

• Journal of Korea Society of Industrial Information Systems
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• v.19 no.6
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• pp.63-76
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• 2014

Recently the desired information has been recommended to many people in a number of ways using the smartphone. Though there are many applications for that purpose, but most applications does not consider the user's current situation. In order to automatically recommend the information considering the user's situation, it is necessary to predict the future behavior of the user from the records of the past behavior of the user. Therefore, in this paper, we propose a method that predicts the user's future behavior through association analysis based on the user's current behavior which is identified by applying the user's current situation data collected via a smartphone to the Bayesian network built from the user's life log. From the experiments and analysis for five students and five virtual workers, the usefulness of the proposed method is confirmed.

• Journal of Information Science Theory and Practice
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• v.10 no.1
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• pp.73-88
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• 2022

This exploratory study aims to understand the potential of log data analysis and expand its utilization in user research methods. Transaction log data are records of electronic interactions that have occurred between users and web services, reflecting information-seeking behavior in the context of digital libraries where users interact with the service system during the search for information. Two ways were used to analyze South Korea's National Digital Science Library (NDSL) log data for three days, including 150,000 data: a log pattern analysis, and log context analysis using statistics. First, a pattern-based analysis examined the general paths of usage by logged and unlogged users. The correlation between paths was analyzed through a χ² analysis. The subsequent log context analysis assessed 30 identified users' data using basic statistics and visualized the individual user information-seeking behavior while accessing NDSL. The visualization shows included 30 diverse paths for 30 cases. Log analysis provided insight into general and individual user information-seeking behavior. The results of log analysis can enhance the understanding of user actions. Therefore, it can be utilized as the basic data to improve the design of services and systems in the digital library to meet users' needs.

• The Journal of the Korea Contents Association
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• v.18 no.7
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• pp.330-337
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• 2018

Recently, there has been a growing interest in user-centered smart city services. In this study, user behavior analysis was performed as a preliminary study for user - centered smart city service planning. In particular, we will use GIS based location analysis data and video ethonography methodology to derive smart city service direction and needs. In this study, the area of Daejeon Design District selected as the Smart City Test bed was selected as the survey area and the location analysis data of the traffic accident analysis system of the road traffic corporation and the fixed camera We observed user's behavior type and change with image data extracted through the technique. Location analysis data is classified according to the type of accident, and image data is classified into 11 subdivided types of user activities. The problems and specificities observed were analyzed. The user behavior characteristics investigated through this study are meaningful to provide a basis for suggesting user - centered smart city services in the future.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.8 no.5
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• pp.121-127
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• 2008

User needs in residential environment have very complicated and variety connection with others. u-home system for the near future is need to be combined acceptance of exist user needs as well as needs on new technology relating with u-Home. The study proposes a User Behavior Analysis Framework - UBAF for applying the user needs to the system more efficiently and developing the system by classifying patterns for the needs based on date of user behavior analysis. UBAF is a developing framework getting the basic idea of combining system modeling methods on SE and user modeling methods considering on HCI. It will be applicable to develop the system with core user behaviors by applying a standard way on u-Home. For example, the study transforms information into knowledge the system modeling and user modeling with analyzing a scenario for indoor temperature controlling on u-Home.

• Journal of Digital Convergence
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• v.10 no.9
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• pp.73-78
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• 2012

The appearance and evolution of cloud service is potentially one of the major advances in information and communication technology. However, it is necessary to identify and understand the various issues of cloud service, both from the perspectives of the providers and the consumers of it. While a lot of studies such as cloud business model, profit model and technology itself are currently taking place in cloud service considering provider's aspects, there are a few researches dealing with cloud service user's aspects. This paper presents the user behavior analysis focused on SaaS users and discusses the development strategy of SaaS service based on the results of user behavior analysis. In order to analyze the user behavior, we surveyed SaaS users divided into two groups such as present and future user groups. Eventually, we proposed the SaaS prospects, development strategy and policy issues based on user behavior analysis.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.5
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• pp.1963-1978
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• 2015

With the advancement of mobile web environments, identification and analysis of the user behavior play a significant role and remains a challenging task to implement with variations observed in the model. This paper presents an efficient method for mining optimized user behavior prediction model using genetic algorithm on mobile web structure. The framework of optimized user behavior prediction model integrates the temporary and permanent register information and is stored immediately in the form of integrated logs which have higher precision and minimize the time for determining user behavior. Then by applying the temporal characteristics, suitable time interval table is obtained by segmenting the logs. The suitable time interval table that split the huge data logs is obtained using genetic algorithm. Existing cluster based temporal mobile sequential arrangement provide efficiency without bringing down the accuracy but compromise precision during the prediction of user behavior. To efficiently discover the mobile users' behavior, prediction model is associated with region and requested services, a method called optimized user behavior Prediction Model using Genetic Algorithm (PM-GA) on mobile web structure is introduced. This paper also provides a technique called MAA during the increase in the number of models related to the region and requested services are observed. Based on our analysis, we content that PM-GA provides improved performance in terms of precision, number of mobile models generated, execution time and increasing the prediction accuracy. Experiments are conducted with different parameter on real dataset in mobile web environment. Analytical and empirical result offers an efficient and effective mining and prediction of user behavior prediction model on mobile web structure.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.7 no.2
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• pp.154-158
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• 2007

As the technology developed, the system is being developed as the structure that is adapted to the intelligent environment. Therefore, the existing situation information system couldn't provide satisfactory service to the user as it provides service only by the information which it received from the sensor. This paper analyzed the problems of the existing user intention awareness system and suggested user intention awareness system to provide a stable and efficient service that fits to the intention of the user compensating this. This paper has collected the behavior data based on the scenario of the sequential behavior course of the user that occurs at breakfast time in the kitchen which is the home domain environment thai is closely related to our lives. This scenario course also showed the flow that the goal intentional user intention awareness system acted that it suggested, and showed the sequential course processing the user behavior data by tables and charts.

• Journal of the Korea Society of Computer and Information
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• v.29 no.5
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• pp.177-187
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• 2024

177-Existing music recommendation systems do not sufficiently consider the discrepancy between the intended emotions conveyed by song lyrics and the actual emotions felt by users. In this study, we generate topic vectors for lyrics and user comments using the LDA model, and construct a user preference model by combining user behavior trajectories reflecting time decay effects and playback frequency, along with statistical characteristics. Empirical analysis shows that our proposed model recommends music with higher accuracy compared to existing models that rely solely on lyrics. This research presents a novel methodology for improving personalized music recommendation systems by integrating emotion recognition and user behavior analysis.