• Journal of the Korean Data and Information Science Society
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• v.14 no.3
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• pp.441-450
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• 2003

Collaborative filtering is one of the most widely used methodologies for recommendation system. Collaborative filtering is based on a data matrix of each customer's preferences and frequently, there exits missing data problem. We introduced two imputation approach (multiple imputation via Markov Chain Monte Carlo method and multiple imputation via bootstrap method) to improve the prediction performance of collaborative filtering and evaluated the performance using EachMovie data.

• Korean Journal of Human Ecology
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• v.15 no.1
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• pp.1-15
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• 2006

The purpose of this study is (1) to investigate how children's peer interaction type and joint problem-solving performance differ, depending on a partner's age, in such a situation as they are asked to solve problems with their peer and (2) to investigate relationship between children's peer interaction type and joint problem-solving performance. Results reveal that children's problem-solving performance receives more benefit in the interactions with older peers, rather than those with younger ones. It can also be improved by higher level of collaborative interactions such as abstract collaborative explanations in joint activities. It is influenced positively by collaborative interactions, expecially when the children are in the same age groups. Results here were discussed in terns Piagetian and Vygotskian theories.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2007.05a
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• pp.218-225
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• 2007

Recommendation system is one way of implementing personalized service. The collaborative filtering is one of the major techniques that have been employed for recommendation systems. It has proven its effectiveness in the recommendation systems for such domain as motion picture or music. However, it has some limitations, i.e., sparsity and scalability. In this research, as one way of overcoming such limitations, we proposed the stepwise collaborative filtering method. To show the practicality of our proposed method, we designed and implemented a movie recommendation system which we shall call Step_CF, and its performance was evaluated using MovieLens data. The performance of Step_CF was better than that of Basic_CF that was implemented using the original collaborative filtering method.

• 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.

• Educational Technology International
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• v.16 no.1
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• pp.1-30
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• 2015

The purpose of this research is to propose a novel social LMS developed for group collaborative learning with a think-aloud tool integrated for sharing cognitive processes in order to improve group collaborative learning performance. In this developmental research, the system was designed with three critical elements: the think-aloud element supports learners through shared cognition, the social network element improves the quality of collaborative learning by forming a structured social environment, and the learning management element provides a understructure for collaborative learning for student groups. Moreover, the three critical elements were combined in an educational context and applied in three directions.

• Educational Technology International
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• v.11 no.2
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• pp.105-121
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• 2010

Collaborative learning encourages the use of high-level cognitive strategies, critical thinking, and interpersonal relationships. Despite these advantages, most instructors reveal the difficulties of using project-based collaborative learning; a common problem is the failure of the group to work effectively together. Thus, this study attempted to provide practical advice on group composition with self-regulation. In a college course, 31 groups with 129 students were asked to discuss and prepare the final presentation material and present it together as a collaborative work. All students' self-regulation skills were measured at the beginning of the semester, and the collective self-regulation was computed as an average of the individual scores of each group. The results of regression analysis indicate that the group's collective self-regulation shows a highly significant positive effect on group performance and satisfaction, as self-regulation predicts individual academic performance. The results also show that there is a significant positive relationship between students' self-regulation and participation in group work.

• Journal of the Korea Society of Computer and Information
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• v.13 no.7
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• pp.19-27
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• 2008

A performance evaluation tool makes an important role in order to improve performance of an embedded software which has restricted computing resources. However, existing performance evaluation tools for embedded softwares cannot be used in collaborative development environment because they support only one developer with performance evaluation work under cross development environment. In this paper, we propose a performance evaluation tool for embedded softwares on collaborative development environment. A proposed tool is based on server and client model. It can have flexibility in offering and integrating the result information for the items. Through the suggested tool. developers can intuitively understand and analysis performance evaluation results each other.

• The KIPS Transactions:PartB
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• v.10B no.3
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• pp.273-280
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• 2003

Recommender System can help users to find products to Purchase. A representative method for recommender systems is collaborative filtering (CF). It predict products that user may like based on a group of similar users. User information is based on user's ratings for products and similarities of users are measured by ratings. As user is increasing tremendously, the performance of the pure collaborative filtering is lowed because of high dimensionality and scarcity of data. We consider the effect of dimension deduction in collaborative filtering to cope with scarcity of data experimentally. We suggest that SVD improves the performance of collaborative filtering in comparison with pure collaborative filtering.

• Proceedings of the KAIS Fall Conference
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• 2003.11a
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• pp.105-110
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• 2003

Collaborative filtering is one of the methodologies that are most widely used for recommendation system. It is based on a data matrix of each customer's preferences of products. There could be a lot of missing values in such preference. data matrix. This incomplete data is one of the reasons to deteriorate the accuracy of recommendation system. Multiple imputation method imputes m values for each missing value. It overcomes flaws of single imputation approaches through considering the uncertainty of missing values.. The objective of this paper is to suggest multiple imputation-based collaborative filtering approach for recommendation system to improve the accuracy in prediction performance. The experimental works show that the proposed approach provides better performance than the traditional Collaborative filtering approach, especially in case that there are a lot of missing values in dataset used for recommendation system.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.10 no.12
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• pp.3645-3652
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• 2009

Most important factor for successful operation of collaborative project, which is a project based curriculum, is systematic and rational assesment. In this paper, we design and implement multidimensional performance assessment support system which can overcome simplicity of result-oriented evaluation. In the proposed system, learner can diagnose problems and improve those by oneself. We design the system satisfying both professor and student about evaluation results through evaluating total course of collaborative project.