• Journal of the Korean Society for Library and Information Science
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• v.48 no.3
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• pp.193-215
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• 2014

This study examines undergraduate nursing students' user satisfaction and affective experience of collaborative information behavior in a group-based learning through survey method to see the relationship between these factors. For this purpose, the affective experiences (positive and negative aspects) of collaborative information search were examined using the PANAS scale through the same questionnaire. Correlation between students' experiences of collaborative information search and affective aspects were examined, and statistical significance using the t-test were also conducted to measure the relationship among students' demographic factors, experiences of collaborative information search and affective aspects. The results revealed that there was a significant correlation between experiences of collaborative information search and affective aspects. Also, there were significant relationships among students' demographic factors, experiences of collaborative information search and affective aspects. In particular, there were signigicant differences in students' overall satisfaction of collaboration and positive aspects between male and female students who experienced collaborative information search. This study found out the possibilities for a follow-up study for affective aspects in collaborative information behavior.

• Journal of Korea Society of Digital Industry and Information Management
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• v.19 no.3
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• pp.105-115
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• 2023

This study analyzed the effects of learning participation motivation and collaborative self-efficacy on knowledge sharing behavior in an online learning environment. Collaborative learning in the online learning environment took the initiative in team formation, learning topic selection, learning planning and execution, and reflection. Collaborative learning was operated as an extracurricular program, and a survey was conducted targeting students who finally completed all learning activities. The results of the study are as follows. First, goal-oriented motivation and self-Efficacy for group work, showed significant influence on knowledge sharing behavior. Second, activity-oriented motivation did not show a statistically significant effect relationship. Interpreting the analysis results, it can be judged that the higher the goal-oriented motivation and self-Efficacy for group work of students who performed collaborative learning in an online learning environment, the higher the willingness to share knowledge, skills, and information they know. This study explored the outcomes of collaborative learning conducted in an online learning environment. It is meaningful that the learner's learning participation motivation was identified and the effect of self-Efficacy for group work, which can be expressed in collaborative learning situations, on knowledge sharing behavior, which is a necessary behavior for group performance, is significant.

• Journal of the Korean Society for Library and Information Science
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• v.47 no.3
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• pp.97-117
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• 2013

This study aimed to explore group members' collaborative information by analyzing the number and the content of text contributions on the online discussion board in the group-based research project. This study explored graduate students' collaborative information behavior, affective approach, and types of collaboration and support needed in the group-based research project based on Kuhlthau's Information Search Process(ISP) Model and Yue and He's Collaborative Information Behavior(CIB) Model. It is expected that the results of this study will be useful for understanding of CIB in the group-based research project and applying information literacy instruction to information user in collaboration.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.9
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• pp.4684-4705
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• 2019

Collaborative filtering recommender systems are vulnerable to shilling attacks in which malicious users may inject biased profiles to promote or demote a particular item being recommended. To tackle this problem, many robust collaborative recommendation methods have been presented. Unfortunately, the robustness of most methods is improved at the expense of prediction accuracy. In this paper, we construct a robust Bayesian probabilistic matrix factorization model for collaborative filtering recommender systems by incorporating the detection of user anomaly rating behaviors. We first detect the anomaly rating behaviors of users by the modified K-means algorithm and target item identification method to generate an indicator matrix of attack users. Then we incorporate the indicator matrix of attack users to construct a robust Bayesian probabilistic matrix factorization model and based on which a robust collaborative recommendation algorithm is devised. The experimental results on the MovieLens and Netflix datasets show that our model can significantly improve the robustness and recommendation accuracy compared with three baseline methods.

• Journal of the Korean Society for information Management
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• v.30 no.3
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• pp.183-205
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• 2013

A number of studies and models have focused on collaborative information behavior (CIB) in various contexts as the importance of research on CIB is recognized in information behavior research. This study compared and analyzed the models of CIB developed by Prekop (2002), Reddy and Jansen (2008), Shah (2008), Karunakaran, Spence and Reddy (2010), and Yue and He (2010), and discussed the direction of future studies of CIB. The future research strategies for overcoming the limitation of previous studies and models on CIB are to extend research object and context, to do holistic research approach tracing complexity of CIB, and to debate and verify the models of CIB. In particular, it needs to modify and improve the models based on the empirical research for domestic context.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2004.11a
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• pp.203-210
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• 2004

Due to the explosion of e-commerce, recommender systems are rapidly becoming a core tool to accelerate cross-selling and strengthen customer loyalty. There are two prevalent approaches for building recommender systems - content-based recommending and collaborative filtering. Collaborative filtering recommender systems have been very successful in both information filtering domains and e-commerce domains, and many researchers have presented variations of collaborative filtering to increase its performance. However, the current research on recommendation has paid little attention to the use of time related data in the recommendation process. Up to now there has not been any study on collaborative filtering to reflect changes in user interest. This paper proposes dynamic fuzzy clustering algorithm and apply it to collaborative filtering algorithm for dynamic recommendations. The proposed methodology detects changes in customer behavior using the customer data at different periods of time and improves the performance of recommendations using information on changes. The results of the evaluation experiment show the proposed model's improvement in making recommendations.

• Journal of Korean Library and Information Science Society
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• v.50 no.4
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• pp.405-428
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• 2019

This study discerned interactions that secondary students experience during their studies and information behaviors that explains their interactions. In addition, when young people collaborate with their friends, this study identified their actions, such as collecting, using, sharing, and generating information, and the social and cultural factors that influence the behavior. The research questions consisted of 'Radical Change Theory' focusing on the information behavior of the digital age youth and the type of radical change in the digital age. As a result of the research, the status of collaborative information behavior of middle and high school students was reflected in our educational reality. Students, however, approached and utilized various information sources, diversified their opinion sharing tools, and discovered channels that were different from adults and used them in information activities. Though not as anticipated, they suggested that the experience of collaboration helped the individual's growth.

• 한국경영정보학회:학술대회논문집
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• 2007.06a
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• pp.576-581
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• 2007

The purpose of this paper is to strengthen trust on the automated collaborative filtering system. Automated collaborative filtering system is quickly becoming a popular technique for recommendation system. This elaborative methodology contributes for reducing information overload and the result becomes index of users' preference. In addition, it can be applied to various industries in various fields. After it collaborative filtering system was developed, many researches are executed to enhance credibility and to apply in various fields. Among these diverse systems, collaborative filtering system which uses Pearson correlation coefficient is most common in many researches. In this paper, we proposed new process diagram of collaborative filtering algorithm and new factors which should improve the credibility of system. In addition, the effects and relationships are also tested.

• Asia pacific journal of information systems
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• v.18 no.4
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• pp.83-103
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• 2008

In the new web based learning environment which has recently emerged, a variety of new learning objectives and teaching methods suited to this learning environment have been adopted. Recently, web based project-based learning methods have received a great deal of attention from those wishing to improve learning performance. The objective of this study is to identify the impact of characteristics of communication media and instruction behavior on collaborative interaction and project performance through web based group projects. The characteristics of communication media were divided into richness, flexibility, and ease of use, and the characteristics of instruction behavior were divided into support and expression, which are independent variables. Collaborative interaction as a mediate variable, was divided into information sharing and negotiation. Project performance was the dependent variable. To verify the proposed research model empirically, an experiment was conducted in which learners participated in on-line and off-line courses with group projects. The group project was conducted virtual product development(VPD), and designed a web-site about the VPD. At the end of the project, a survey was conducted. Of the 270 students, 239 responded. The students were assigned to groups of 3 or 4 members, and represented different genders and levels of computer competence. The reliability, validity, and correlation of research variables were analyzed using SPSS 14.0, and the measurement model and the structural goodness-of-fit of the research model were verified through SEM analysis using Lisrel 8.54. We found important results as follows; First, richness and ease of use has positive impacts on each of sharing information and negotiation. This suggests that richness and ease of use are useful in sharing information which is related to the task and agreeing in opinions among group members. However, flexibility has not positive impacts on sharing information and negotiation. This implies that there is no great difference in performance of PC and information literacy of user. Second, support and expression of instructor have positive impacts on sharing information and negotiation. This indicates that instructors play an important role in encouraging learners to participate in the project and communicating with them, sharing information related to the project, making a resonable decision and finally leading them to improve a project performance. Third, collaborative interaction has a positive impact on project performance. This result shows that if the ability to share information and negotiate among students was improved then a project performance would be improved as well. Recently, in the state of revitalized web based learning, it is opportune that web-based group project is practically conducted, and the impact of characteristics of communication media and characteristics of instruction behavior on sharing information, negotiating among group members and improving a project performance is verified. On the basis of these results, we propose that forms of learning, such as web based project, could be one of solution which is to enforce interaction among learners, and ultimately improve learning performance. Moreover web-based group project is able to make up for a weakness which makes it difficult to make interpersonal relations or friendship among learners in computer mediated communication or web based learning.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.4
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• pp.1643-1660
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• 2016

Connectivity and trust in social networks have been exploited to propose applications on top of these networks, including routing, Sybil defenses, and anonymous communication systems. In these networks, and for such applications, connectivity ensures good performance of applications while trust is assumed to always hold, so as collaboration and good behavior are always guaranteed. In this paper, we study the impact of differential behavior of users on performance in typical social network-enabled routing applications. We classify users into either collaborative or rational (probabilistically collaborative) and study the impact of this classification and the associated behavior of users on the performance of such applications, including random walk-based routing, shortest path based routing, breadth-first-search based routing, and Dijkstra routing. By experimenting with real-world social network traces, we make several interesting observations. First, we show that some of the existing social graphs have high routing costs, demonstrating poor structure that prevents their use in such applications. Second, we study the factors that make probabilistically collaborative nodes important for the performance of the routing protocol within the entire network and demonstrate that the importance of these nodes stems from their topological features rather than their percentage of all the nodes within the network.