• The Journal of Society for e-Business Studies
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• v.17 no.2
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• pp.129-147
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• 2012

Collaborative filtering (CF), one of the most successful recommendation techniques, has been used in a number of different applications such as recommending web pages, movies, music, articles and products. One of the critical issues in CF is why recommendation performances are different depending on application domains. However, prior literatures have focused on only data characteristics to explain the origin of the difference. Scant attentions have been paid to provide systematic explanation on the issue. To fill this research gap, this study attempts to systematically explain why recommendation performances are different using structural indexes of social network. For this purpose, we developed hypotheses regarding the relationships between structural indexes of social network and recommendation performance of collaboration filtering, and empirically tested them. Results of this study showed that density and inconclusiveness positively affected recommendation performance while clustering coefficient negatively affected it. This study can be used as stepping stone for understanding collaborative filtering recommendation performance. Furthermore, it might be helpful for managers to decide whether they adopt recommendation systems.

• The KIPS Transactions:PartB
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• v.10B no.3
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• pp.287-296
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• 2003

There has been much research focused on collaborative filtering technique in Recommender System. However, these studies have shown the First-Rater Problem and the Sparsity Problem. The main purpose of this Paper is to solve these Problems. In this Paper, we suggest the user's predicting preference method using Bayesian estimated value and the associative user clustering for the recalculation of preference. In addition to this method, to complement a shortcoming, which doesn't regard the attribution of item, we use Representative Attribute-Neighborhood method that is used for the prediction when we find the similar neighborhood through extracting the representative attribution, which most affect the preference. We improved the efficiency by using the associative user's clustering analysis in order to calculate the preference of specific item within the cluster item vector to the collaborative filtering algorithm. Besides, for the problem of the Sparsity and First-Rater, through using Association Rule Hypergraph Partitioning algorithm associative users are clustered according to the genre. New users are classified into one of these genres by Naive Bayes classifier. In addition, in order to get the similarity value between users belonged to the classified genre and new users, and this paper allows the different estimated value to item which user evaluated through Naive Bayes learning. As applying the preference granted the estimated value to Pearson correlation coefficient, it can make the higher accuracy because the errors that cause the missing value come less. We evaluate our method on a large collaborative filtering database of user rating and it significantly outperforms previous proposed method.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.10
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• pp.445-454
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• 2023

Recently, various fault diagnosis studies are being conducted utilizing data from collaborative robots. Existing studies performing fault diagnosis on collaborative robots use static data collected based on the assumed operation of predefined devices. Therefore, the fault diagnosis model has a limitation of increasing dependency on the learned data patterns. Additionally, there is a limitation in that a diagnosis reflecting the characteristics of collaborative robots operating with multiple joints could not be conducted due to experiments using a single motor. This paper proposes an LSTM diagnostic model that can overcome these two limitations. The proposed method selects representative normal patterns using the correlation analysis of vibration and current data in single-axis and multi-axis work environments, and generates residual patterns through differences from the normal representative patterns. An LSTM model that can perform gear wear diagnosis for each axis is created using the generated residual patterns as inputs. This fault diagnosis model can not only reduce the dependence on the model's learning data patterns through representative patterns for each operation, but also diagnose faults occurring during multi-axis operation. Finally, reflecting both internal and external data characteristics, the fault diagnosis performance was improved, showing a high diagnostic performance of 98.57%.

• Journal of Information Technology Applications and Management
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• v.24 no.1
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• pp.169-185
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• 2017

This study proposes a novel recommendation algorithm that reflects the results from trust/distrust network analysis as a solution to enhance prediction accuracy of recommender systems. The recommendation algorithm of our study is based on memory-based collaborative filtering (CF), which is the most popular recommendation algorithm. But, unlike conventional CF, our proposed algorithm considers not only the correlation of the rating patterns between users, but also the results from trust/distrust relationship network analysis (e.g. who are the most trusted/distrusted users?, whom are the target user trust or distrust?) when calculating the similarity between users. To validate the performance of the proposed algorithm, we applied it to a real-world dataset that contained the trust/distrust relationships among users as well as their numeric ratings on movies. As a result, we found that the proposed algorithm outperformed the conventional CF with statistical significance. Also, we found that distrust relationship was more important than trust relationship in measuring similarities between users. This implies that we need to be more careful about negative relationship rather than positive one when tracking and managing social relationships among users.

• Proceedings of the Korea Information Processing Society Conference
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• 2019.05a
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• pp.267-269
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• 2019

There are many recommendation systems offer an effort to get better preciseness the information to the users. In order to further improve more accuracy, the social network analysis method which is used to analyze data to community detection in social networks was introduced in the recommendation system and the result shows this method is improving more accuracy. In this paper, we propose a movie recommendation system using social network analysis and normalized discounted cumulative gain with the best accuracy. To estimate the performance, the collaborative filtering using the k nearest neighbor method, the social network analysis with collaborative filtering method and the proposed method are used to evaluate the MovieLens data. The performance outputs show that the proposed method get better the accuracy of the movie recommendation system than any other methods used in this experiment.

• Journal of Information Technology Applications and Management
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• v.25 no.4
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• pp.185-196
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• 2018

This study investigates the social network among authors to improve the quality of Panel researches. Korea Health Panel (KHP), implemented by the collaborative work between KIHASA (Korea Institute for Health and Social Affairs) and NHIC (National Health Insurance Service) since 2008, provides a critical infrastructure for policy making and management for insurance system and healthcare service. Using bibliographic data extracted from academic databases, eighty articles were extracted in domestic and international journals from 2008 to 2014, April. Data were analyzed by NetMiner 4.0, social network analysis software, to identify the extent to which authors are involved in healthcare use research and the patterns of collaboration between them. Analysis reveals that most authors publish a very small number of articles and collaborate within tightly knit circles. Centrality measures confirm these findings by revealing that only a small percentage of the authors are structurally dominant, and influence the flow of communication among others. It leads to the discovery of dependencies between the elements of the co-author network such as affiliates in health panel communities. Based on these findings, we recommend that Korea Health Panel could benefit from cultivating a wider base of influential authors and promoting broader collaborations.

• Journal of The Korean Association For Science Education
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• v.35 no.6
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• pp.1049-1062
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• 2015

To understand the synergy of collaboration and to apply this understanding to education, an analysis of how a team solves a problem and the sharing of their mental models is needed. This paper analyzed two things qualitatively to find out the source of synergy in a collaborative problem-solving process. First, the sharing contents in team mental model and second, the process of sharing the team mental model. Ten gifted middle school students collaborated to solve an ill-defined problem called sunshine through foliage problem. The gifted students shared the following results after the collaboration: First, scientific concept prior to common idea or the idea that all group members have before the discussions; second, unique individual ideas of group members; and third, created ideas that were not originally in the personal mental model. With created ideas, the team model becomes more than the sum of individuals. According to the results of process analysis, in the process of sharing mental model, the students proposed and shared the most important variable first. This result implied that the analysis of the order of sharing ideas is important as much as finding shared ideas. Also, the result shows that through their collaboration, the gifted students' shared mental model became more refined and expanded as compared to their individual prior mental models. It is recommended that these results can be used to measure shared mental model and develop collaborative learning models for students.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.12
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• pp.383-392
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• 2018

This study was conducted to examine the effects of a meta-cognition teaching and learning program on nursing students' self-leadership, collaborative preference and problem solving ability. The study was designed using a nonequivalent control group pretest-posttest design. Data were collected between March 5 and June 30, 2018 from 74 2-year nursing students in D College of D City who were assigned to an experimental group (n=36) or a control group (n=38). The meta-cognition teaching and learning program consisted of 10 sessions of combined individual and small group learning. Data were analyzed using descriptive analysis, as well as a t-test, ${\chi}^2$-test, and paired t-test with the SPSS/WIN 21.0 program. After receiving the meta-cognition teaching and learning program, significant differences were observed in self-leadership (t=4.79, p<0.001), collaborative preference (t=5.07, p<0.001), and problem-solving ability (t=6.48, p<0.001) of the experimental group. The results of this study indicate that the meta-cognition teaching and learning program was effective at increasing self-leadership, collaborative preference and problem-solving ability in nursing students. It is expected that the results of this study will be used as basic data to improve self-leadership, collaborative preference and problem-solving ability of nursing students.

• Korean Journal of Child Education & Care
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• v.17 no.4
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• pp.163-185
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• 2017

This study set out to investigate the joint homeroom teacher system with two regular teachers at early childhood education institutions in a realistic manner, thus contributing to its application to the field, establishing a system of teachers with professionalism, and providing basic data to create and manage the collaborative capabilities of teachers. For these purposes, the investigator collected and analyzed data from 13 semi-structured individual and group interviews with 16 teachers at K Kindergarten in Gyeonggi Province, eight field observations, and four participant observations for about three months from April to July, 2017. The data were analyzed in the stages of qualitative data analysis involving keyword categories, classification, and discovery of sub-themes. Based on the findings, the study categorized the collaborative management of the joint homeroom teacher system with two regular teachers into "job performance," "difficulties," "institutional supports" and "changes." These findings lead to an expectation that the introduction of the joint homeroom teacher system with two regular teachers will establish a foundation for higher quality of education through the process and changes of collaborative management between two teachers with professionalism.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.46 no.6
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• pp.56-65
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• 2009

The single device that is combined more than one communication systems in shared band is called collocated system. In this paper, the collocated system is made by combination of Wi-Fi and WiMAX system. Performance of the collocated system is analysed by using two communication model, Collaborative and Non-Collaborative. To minimize the mutual interference between Wi-Fi and WiMAX system in collocated system, the circulator-based collocated system is proposed and analysed it's performance characteristics.