• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.8
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• pp.3328-3349
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• 2020

With rapid development of ubiquitous computing and location-based services (LBSs), human trajectory data and associated activities are increasingly easily recorded. Inappropriately publishing trajectory data may leak users' privacy. Therefore, we study publishing trajectory data while preserving privacy, denoted privacy-preserving activity trajectories publishing (PPATP). We propose S-PPATP to solve this problem. S-PPATP comprises three steps: modeling, algorithm design and algorithm adjustment. During modeling, two user models describe users' behaviors: one based on a Markov chain and the other based on the hidden Markov model. We assume a potential adversary who intends to infer users' privacy, defined as a set of sensitive information. An adversary model is then proposed to define the adversary's background knowledge and inference method. Additionally, privacy requirements and a data quality metric are defined for assessment. During algorithm design, we propose two publishing algorithms corresponding to the user models and prove that both algorithms satisfy the privacy requirement. Then, we perform a comparative analysis on utility, efficiency and speedup techniques. Finally, we evaluate our algorithms through experiments on several datasets. The experiment results verify that our proposed algorithms preserve users' privay. We also test utility and discuss the privacy-utility tradeoff that real-world data publishers may face.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.43-56
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• 2020

Recommender systems help users make the best choice among various options. Especially, recommender systems play important roles in internet sites as digital information is generated innumerable every second. Many studies on recommender systems have focused on an accurate recommendation. However, there are some problems to overcome in order for the recommendation system to be commercially successful. First, there is a lack of transparency in the recommender system. That is, users cannot know why products are recommended. Second, the recommender system cannot immediately reflect changes in user preferences. That is, although the preference of the user's product changes over time, the recommender system must rebuild the model to reflect the user's preference. Therefore, in this study, we proposed a recommendation methodology using topic modeling and sequential association rule mining to solve these problems from review data. Product reviews provide useful information for recommendations because product reviews include not only rating of the product but also various contents such as user experiences and emotional state. So, reviews imply user preference for the product. So, topic modeling is useful for explaining why items are recommended to users. In addition, sequential association rule mining is useful for identifying changes in user preferences. The proposed methodology is largely divided into two phases. The first phase is to create user profile based on topic modeling. After extracting topics from user reviews on products, user profile on topics is created. The second phase is to recommend products using sequential rules that appear in buying behaviors of users as time passes. The buying behaviors are derived from a change in the topic of each user. A collaborative filtering-based recommendation system was developed as a benchmark system, and we compared the performance of the proposed methodology with that of the collaborative filtering-based recommendation system using Amazon's review dataset. As evaluation metrics, accuracy, recall, precision, and F1 were used. For topic modeling, collapsed Gibbs sampling was conducted. And we extracted 15 topics. Looking at the main topics, topic 1, top 3, topic 4, topic 7, topic 9, topic 13, topic 14 are related to "comedy shows", "high-teen drama series", "crime investigation drama", "horror theme", "British drama", "medical drama", "science fiction drama", respectively. As a result of comparative analysis, the proposed methodology outperformed the collaborative filtering-based recommendation system. From the results, we found that the time just prior to the recommendation was very important for inferring changes in user preference. Therefore, the proposed methodology not only can secure the transparency of the recommender system but also can reflect the user's preferences that change over time. However, the proposed methodology has some limitations. The proposed methodology cannot recommend product elaborately if the number of products included in the topic is large. In addition, the number of sequential patterns is small because the number of topics is too small. Therefore, future research needs to consider these limitations.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.14 no.1
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• pp.173-198
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• 2003

The purpose of this study is to provide suggestions for the design of fashion fields’ websites that can help users conduct their researches. The evaluation criteria were developed and the usability tests of websites in fashion fields were conducted by user groups. In addition, the questionnaires were distributed to user groups in fashion fields to collect data associated with users' information needs and their information use behaviors. Following an analysis, a model of the effective websites design in fashion fields is provided.

• Journal of Information Science Theory and Practice
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• v.8 no.3
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• pp.30-46
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• 2020

This research aimed (1) to study and analyze the ability of current information retrieval (IR) systems based on views of information behavior (IB), and (2) to propose a conceptual framework for an IB model based on the collaboration between the system and user, with the intent of developing an IR system that can apply intelligent techniques to enhance system efficiency. The methods in this study consisted of (1) document analysis which included studying the characteristics and efficiencies of the current IR systems and studying the IB models in the digital environment, and (2) implementation of the Delphi technique through an indepth interview method with experts. The research results were presented in three main parts. First, the IB model was categorized into eight stages, different from traditional IB, in the digital environment, which can correspond to all behaviors and be applied to with an IR system. Second, insufficient functions and log file storage hinder the system from effectively understanding and accommodating user behavior in the digital environment. Last, the proposed conceptual framework illustrated that there are stages that can add intelligent techniques to the IR system based on the collaboration perspective between the user and system to boost the users' cognitive ability and make the IR system more user-friendly. Importantly, the conceptual framework for the IB model based on the collaboration perspective between the user and system for IR assisted the ability of information systems to learn, recognize, and comprehend human IB according to individual characteristics, leading to enhancement of interaction between the system and users.

• Journal of Information Management
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• v.41 no.3
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• pp.85-104
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• 2010

This study tries to survey general behaviors of the users and their's perceptions on the service quality, with the staff members' expectations, of the Information Commons in the National Digital Library of Korea. 'Computer Cluster,' 'User guide service' and 'Internet information' are most used and general facilities, services, and contents are most satisfied. The comparative analysis shows that the customers' perceptions of overall satisfaction, customer loyalty, and service quality and overall satisfaction of each three primary dimensions(library staff, resources and services, facility and physical environment) are higher than those of staff members' expectations.

• Journal of Digital Convergence
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• v.19 no.6
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• pp.1-9
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• 2021

Artificial intelligence (AI) devices are rapidly emerging as a core platform of next-generation information and communication technology (ICT), this study investigated consumer usage behavior and user experience through AI devices that are widely applied to consumers' daily lives. To this end, data was collected from 600 consumers with experience in using AI devices were derived to recognize the attributes and behavior of AI devices. The analysis results are as follows. First, music listening was the most used among various attributes and it was found that simple functions such as providing weather information were usefully recognized. Second, the main devices used by AI device users were identified as AI speakers, smartphone, PC and laptops. Third, associative images of AI devices appeared in the order of fun, useful, novel, smart, innovative, and friendly. Therefore, practical implications are suggested to contribute to provision of user services using AI devices in the future by analyzing usage behaviors that reflect the characteristics of AI devices.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.54 no.3
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• pp.107-116
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• 2005

The paper addresses modeling and analysis of a grid-connected photovoltaic generation system (PV system). PSCAD/EMTDC, an industry standard simulation tool for studying the transient behavior of electric power system and apparatus, is used to conduct all aspects of model implementation and to carry out extensive simulation study. This paper is aimed at sharing with the PSCAD/EMTDC user community our user-defined model for PV system applications, which is not yet available as a standard model within PSCAD/EMTDC. An equivalent circuit model of a solar cell has been used for modeling solar array. A series of parameters required for array modeling have been estimated from general specification data of a solar module. A PWM voltage source inverter (VSI) and its current control scheme have been implemented. A maximum power point tracking (MPPT) technique is employed for drawing the maximum available energy from the PV array. Comprehensive simulation results are presented to examine PV array behaviors and PV system control performance in response to irradiation changes. In addition, dynamic responses of PV array and system to network fault conditions are simulated and analysed.

• The Journal of Information Systems
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• v.27 no.2
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• pp.115-134
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• 2018

Purpose In recent years, mobile payment service users have been rapidly increasing. Previous researchers focused on the mobile usage situation such as the elements of mobile payment service, usage pattern, and user behaviors, and the research that is approached from the viewpoint of the user is still insufficient. The aim of this study is to suggest a acceptance-resistance motivation model of choosing a mobile payment service based on the Herzbergs Two-Factor Theory by investigating users' motivation and hygiene factors. Design/methodology/approach For the purpose, literature reviews on factors of choosing a mobile payment service were conducted and classified motivation and hygiene factors. Two hypotheses were set as follows: Hypothesis I is that motivation factors have a positive impact on the choice of mobile payment service, and Hypothesis II is that hygiene factors have a negative impact on the choice of mobile payment service. To test two hypotheses, this study conducted an online questionnaire survey and a multivariate discriminant analysis. Findings The result found that mobile payment service is more likely to be replaced with mobile by improving convenience, simplicity, and ease of use that affect the acceptance motivation of mobile payment service. This result supported the Hypothesis I but not Hypothesis II and contributed to provide implications for future mobile payment service development and marketing utilization.

• Journal of Digital Contents Society
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• v.19 no.4
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• pp.711-717
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• 2018

The breakthrough in computer and network has facilitated a variety of information exchange. However, at the same time, malicious users and groups are attacking vulnerable systems. Intrusion Detection System(IDS) detects malicious behaviors through network packet analysis. However, it has a burden of processing a large amount of packets in a short time. Therefore, in order to solve these problem, we propose a network intrusion detection system that operates at kernel level to improve detection performance at user level. In fact, we confirmed that the network intrusion detection system implemented at kernel level improves packet analysis and detection performance.

• International Commerce and Information Review
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• v.11 no.3
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• pp.101-123
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• 2009

This research which applied with Uses and Gratification Theory tried the comparative analysis between internet purchasers and internet non-purchasers by the user's motivation, attitudes, and behavior in Mongolia, By utilizing the two-group discriminant analysis method, which tested the hypotheses of this study. In Internet Market of Mongolia, This research classifies all internet consumers into internet purchasers, and non-internet purchasers and examines the differences in motivations, behaviors, and attitudes between the two groups; based on the assumption that these two groups have different needs and expectations while using the internet. The two group discriminant analysis was conducted to identify a lot of differences between the two groups. Research results show that important differences are found in motivations for using the Internet, attitudes toward the Internet, amount of Internet usage, and frequency of visiting a certain type of Web site. In the practical aspect, This result provides an understanding of the Mongolia Internet shopping, also it could give some valuable implication for the Internet company marketers who are trying to find out how to penetrate into Mongolia internet market.