• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.7 no.6
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• pp.777-784
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• 2017

In the architectural space, the user's behavior is the most important factor in evaluating the quality of architecture. Normally, the evaluation of user behavioral performance was carried out after a building was completed. Recently, interest in and efforts at pre-simulation based on information technology are accelerating. However, since existing user simulation technology is concerned mainly with simply escaping from a large space, it is impossible to simulate the behavior of multiple users in an architectural space. The present study strives to develop a human-figured intelligent agent for advanced user simulation based on ontology. The main purpose of the study is to employ the intelligent behaviors of a NPC(Non-player Character) to infer the ontology of both spatial and user information. In this paper, we intend to integrate ontology inference technology into the virtual space. And also, this study suggest the ontology visualization technology which illustrate the ontology-based information and their change in the spatial information.

• Journal of Information Science Theory and Practice
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• v.1 no.4
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• pp.48-66
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• 2013

The aim of this study is to examine the user's online searching behavior in IR interfaces of e-journal database systems. The study is purely based on survey methods and tries to analyse the online searching behavior of respondents of social science disciplines who were doing research in three target central universities of Delhi (i.e. DU, JMI, and JNU). For measuring the responses of the respondents in IR interfaces of e-journal database systems, a total of 396 questionnaires were distributed among the students and out of all, 305 responses were used for the study. The findings of the study reveal that most of the students were not using all the facilities offered in IR interfaces of e-journal database systems for their retrieval process and also encourages menu based searches rather than command based searching.

• Journal of Information Processing Systems
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• v.14 no.2
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• pp.523-538
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• 2018

Keystroke dynamics user authentication is a behavior-based authentication method which analyzes patterns in how a user enters passwords and PINs to authenticate the user. Even if a password or PIN is revealed to another user, it analyzes the input pattern to authenticate the user; hence, it can compensate for the drawbacks of knowledge-based (what you know) authentication. However, users' input patterns are not always fixed, and each user's touch method is different. Therefore, there are limitations to extracting the same features for all users to create a user's pattern and perform authentication. In this study, we perform experiments to examine the changes in user authentication performance when using feature vectors customized for each user versus using all features. User customized features show a mean improvement of over 6% in error equal rate, as compared to when all features are used.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.40 no.4
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• pp.154-163
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• 2017

Collaborative filtering, one of the most widely used techniques to build recommender systems, is based on the idea that users with similar preferences can help one another find useful items. Credit card user behavior analytics show that most customers hold three or less credit cards without duplicates. This behavior is one of the most influential factors to data sparsity. The 'cold-start' problem caused by data sparsity prevents recommender system from providing recommendation properly in the personalized credit card recommendation scenario. We propose a personalized credit card recommender system to address the cold-start problem, using multiple user profiles. The proposed system consists of a training process and an application process using five user profiles. In the training process, the five user profiles are transformed to five user networks based on the cosine similarity, and an integrated user network is derived by weighted sum of each user network. The application process selects k-nearest neighbors (users) from the integrated user network derived in the training process, and recommends three of the most frequently used credit card by the k-nearest neighbors. In order to demonstrate the performance of the proposed system, we conducted experiments with real credit card user data and calculated the F1 Values. The F1 value of the proposed system was compared with that of the existing recommendation techniques. The results show that the proposed system provides better recommendation than the existing techniques. This paper not only contributes to solving the cold start problem that may occur in the personalized credit card recommendation scenario, but also is expected for financial companies to improve customer satisfactions and increase corporate profits by providing recommendation properly.

• Journal of Information Technology Applications and Management
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• v.17 no.2
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• pp.175-185
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• 2010

The primary objective of this paper is to examine post acceptance behavior model in UCC services. Based on the relevant literature reviews, this study posits six characteristics, that is, expectation confirmation, perceived usefulness, perceived enjoyment, satisfaction, and continuance intention as key variables to describe the post acceptance behavior model in UCC services. Then we constructed a research model and hypotheses about relationship between these variables. A total 286 usable survey responses of UCC service users have been employed in the analysis. The empirical results of this study are summarized as follows. Firstly, expectation confirmation has a positive effect on the perceived usefulness, perceived enjoyment, and satisfaction. Secondly, perceived enjoyment has a positive effect on the satisfaction. Lastly, perceived usefulness, perceived enjoyment, and satisfaction have a positive effect on the continuance intention. These results will be helpful for the UCC services sector to further develop service delivery strategies for strengthening the ongoing relationship with customers. Finally, some possible limits of the present research and future directions for such research were discussed.

• Journal of People, Plants, and Environment
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• v.22 no.5
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• pp.525-539
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• 2019

Much research has been conducted on user behavior by taking surveys and interviews to plan the green space effectively. However, there is a limitation in understanding detailed user characteristics such as personalities and values. This study applied the Persona-based Scenario Method (PSM) to Hongneung Experimental Forest to understand the detailed needs and behaviors of the users in the forest recreation area. The PSM is a user experience modeling technique, which tries to understand the users by describing the type of users as real people. This study 1) extracts the factors of visitor recreation activities in Hongneung Experimental Forest based on the results of the survey, 2) develops user personas based on the results of survey and comes up with activity factors, and 3) designs user scenarios. As a result of applying the PSM, 64 factors of visitor activities were derived from the observation survey in 14 sites of Hongneung Experimental Forest and 25 key factors of visitor activities were chosen through observer's brainstorming. Second, three types of personas were developed considering the key factors and the results of user characteristics with quantitative and qualitative analysis. Lastly, context scenarios were designed by applying the key factors of visitor recreation activities to the persona model. We identified the design problems of the space and design requirements through the scenarios. This study has significance in that it takes an approach from the user perspective and was applied to the forest recreation area, which was mainly used in product design. The developed personas could be used for deriving design elements and setting the direction for planning considering detailed needs, behaviors and characteristics of users.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.10
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• pp.897-907
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• 2020

The development of information technology and the demands of society on an application in an operating system, as well as increasing the specifications and sophistication of smartphones, require banks to make changes to their mobile banking applications. The transformation of the mobile banking application into a digital banking application conducted by banks has made users re-evaluate based on their preferences. This study presents the behavior of users of digital banking applications in Yogyakarta, Indonesia. The hypothesis model is based on Technology Acceptance Model (TAM) with additional factors of the social image, perceived risk and perceived trust adopted from Muñoz-Leiva et al. (2017). The methodology in this study includes data collection through questionnaires distributed online and data analysis using the Structural Equation Model. The results of this study illustrate that the perceived trust and perceived risk have a more dominant part in influencing user attitude and user intention to use digital banking. Meanwhile, social image, perceived ease-of-use and perceived usefulness are not significant in influencing user attitude and user intention to use digital banking. The implication of this research helps to determine the right communication and strategy so that more users with more benefits can utilize this digital banking application.

• Journal of the Korean Society for Library and Information Science
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• v.48 no.3
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• pp.55-70
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• 2014

Although a significant number of studies have been conducted in user-based relevance criteria, a need for further research still remains. The rational is associated with the following inadequacies: (1) research on young user groups, (2) research on the Web environment with multimedia resources, (3) research on the irrelevance criteria and implications to improve related systems and services. Accordingly, this study identified user - based relevance and irrelevance criteria, examining 40 middle school third grader students who use KERIS Edunet site. The results identified 16 relevance criteria and 8 irrelevance criteria. Major implications related to information system and service improvements.

• Journal of the Korean Society for information Management
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• v.26 no.1
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• pp.81-105
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• 2009

In this paper, we propose user contents using behavior and location information on contents on various channels, such as web, IPTV, for contents distribution. With methods to build user and contents profiles, contents using behavior as an implicit user feedback was applied into machine learning procedure for updating user profiles and contents preference. In machine learning procedure, contents-based and collaborative filtering methods were used to analyze user's contents preference. This study proposes contents location information on web sites for final recommendation contents as well. Finally, we refer to a generalized recommender system for personalization. With those methods, more effective and accurate recommendation service can be possible.

• The Journal of the Convergence on Culture Technology
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• v.7 no.1
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• pp.454-460
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• 2021

This study attempted to empirically analyze how the use of portal news is related with user's social credibility levels. 1100 subjects were sampled and online-surveyed. The amount of news usage and level of social credibility were measured respectively by the PNRBS(Portal News Readership Behavior Score) and SCI(Social Credibility Index) which were developed by the researcher. The results show that the relationship between scocial credibility and portal new use is not a linear but some V type. In terms of the mean score of social credibility index, non-portal news user is the highest while the light user is the lowest and the heavy user is the second highest.