• Journal of Korea Game Society
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• v.18 no.1
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• pp.105-114
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• 2018

Various data analysis methods used for customer type analysis are very important for game companies to understand their type and characteristics in an attempt to plan customized content for our customers and to provide more convenient services. In this paper, we propose a k-mode cluster analysis algorithm that uses information uncertainty by extending information entropy to reduce information loss. Therefore, the measurement of the similarity of attributes is considered in two aspects. One is to measure the uncertainty between each attribute on the center of each partition and the other is to measure the uncertainty about the probability distribution of the uncertainty of each property. In particular, the uncertainty in attributes is taken into account in the non-probabilistic and probabilistic scales because the entropy of the attribute is transformed into probabilistic information to measure the uncertainty. The accuracy of the algorithm is observable to the result of cluster analysis based on the optimal initial value through extensive performance analysis and various indexes.

• Korean Journal of Culture and Social Issue
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• v.29 no.4
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• pp.595-616
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• 2023

The Critical Consciousness Scale (CCS) is a scale developed by Diemer and colleagues (2017) that can measure the capacity of the oppressed or marginalized people to critically analyze their social and political conditions, support societal equality, and take action to change the perceived inequities. In this study, we validated the CCS for Korea by adapting and localizing the scale and validating it among university students. Content validity was verified by having five individuals with master's and doctoral degrees in psychology evaluate the suitability of the translated items. Afterwards, reliability and validity were verified through a survey of 314 university students nationwide using the CCS, along with the opportunity inequality recognition scale, recognition of the need for environmental change scale, social participation scale, and belief in a just world scale. To verify the scale's validity, exploratory factor analysis was conducted, confirming three subfactors. Then, a confirmatory factor analysis was carried out, where 14 items out of the original 22 were retained. The construct validity and reliability of these 14 items were found to be satisfactory. Additionally, in the correlation analysis between the CCS and similar scales, a significant clear relationship was found. The CCS showed a positive correlation with scales such as opportunity inequality recognition, need for environmental change recognition, and social participation, and a negative correlation with the belief in a just world scale. Based on these results, the CCS can be considered valid and reliable. Finally, the limitations and significance of this study were discussed.

• Korean Journal of Culture and Social Issue
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• v.9 no.2
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• pp.41-59
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• 2003

The present study was conducted to (1) explore the perceptions of Korean men and women about what is an important relationship for them and how do each gender group construe relational self, and (2) develop the scale to assess the factors of relational self and verify construct validity of the scale. 40 college students and 60 adults participated in survey and FGI (Focused Group Interview) respectively, and content analysis of their responses yielded 2 categories with 39 characteristics of relational self. The one category was named 'instrumentality' which was important to men and the other was named 'expressivity' which was important to women. The list of 39 items was administered to a nationwide sample of 1503 Korean adults to assess their construal of relational self through the 6-point Likert scale. Principal axis factor analysis showed that the two categories were unidimensional with high reliability. As a result of factor analysis on each category, a total of 9 factors were extracted. Specifically, the instrumentality consisted of factors such as utilitarianism, independence, initiativeness, self-assurance, and competence. And the factors of expressivity were empathy, passiveness, dependency, consideration. The tests of mean difference revealed that men had higher scores in most of the instrumental factors, while women had higher scores in most of the expressive factors. But there was no sex difference in the interdependent self-construal scale(Cross, 2000) which has been frequently used for measuring relational self. This is related to the Korean's collective cultural characteristics, and it was concluded that the relationship with others is very important to both Korean men and women, but the meaning and expectation of the relationship as well as the method for its preservation are different to each sex group. In addition, the correlation analyses indicated that the feminity score was positively correlated with the expressiveness while the masculinity score was positively correlated with instrumentality. This result implicated the differences of relational self among Korean people were related to the socialization process of each sex, i.e., sex role identity. Finally, limitations of this study and the directions for future research were discussed.

• Journal of the Korean association of regional geographers
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• v.11 no.4
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• pp.497-510
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• 2005

The purpose of this study was to examine the image similarity, preference, and the attribute recognition using multidimensional scaling. The analyses were carried out by 5 spa destinations located in Choongchung area. The results were as followings: first, considering the image similarity, the image of Suanbo & Onyang and Dogo & Asan were similar except for the Yusung. Second, considering the attribute recognition, Yusung had a stronger attribute reflecting spa tradition when compared to other competitive spa destinations. Onyang showed a strong attribute of facilities. Dogo had a stronger point about use cost. Suanbo had relatively strong attributes in terms of facilities, customer service, and accessability. However, the water quality of spa destination and activities were not reflected in attribute recognition because these two attributes was farthest from the spa destination. Third, considering the preference of selecting spa destinations, package tourists had a strong preference about Yusung, individual tourists, family, incentive tourists prefer Suanbo, followed by Dogo and Yusung. Group tourists had a strong preference about Dogo.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.23-46
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• 2021

Collaborative filtering, which is often used in personalization recommendations, is recognized as a very useful technique to find similar customers and recommend products to them based on their purchase history. However, the traditional collaborative filtering technique has raised the question of having difficulty calculating the similarity for new customers or products due to the method of calculating similaritiesbased on direct connections and common features among customers. For this reason, a hybrid technique was designed to use content-based filtering techniques together. On the one hand, efforts have been made to solve these problems by applying the structural characteristics of social networks. This applies a method of indirectly calculating similarities through their similar customers placed between them. This means creating a customer's network based on purchasing data and calculating the similarity between the two based on the features of the network that indirectly connects the two customers within this network. Such similarity can be used as a measure to predict whether the target customer accepts recommendations. The centrality metrics of networks can be utilized for the calculation of these similarities. Different centrality metrics have important implications in that they may have different effects on recommended performance. In this study, furthermore, the effect of these centrality metrics on the performance of recommendation may vary depending on recommender algorithms. In addition, recommendation techniques using network analysis can be expected to contribute to increasing recommendation performance even if they apply not only to new customers or products but also to entire customers or products. By considering a customer's purchase of an item as a link generated between the customer and the item on the network, the prediction of user acceptance of recommendation is solved as a prediction of whether a new link will be created between them. As the classification models fit the purpose of solving the binary problem of whether the link is engaged or not, decision tree, k-nearest neighbors (KNN), logistic regression, artificial neural network, and support vector machine (SVM) are selected in the research. The data for performance evaluation used order data collected from an online shopping mall over four years and two months. Among them, the previous three years and eight months constitute social networks composed of and the experiment was conducted by organizing the data collected into the social network. The next four months' records were used to train and evaluate recommender models. Experiments with the centrality metrics applied to each model show that the recommendation acceptance rates of the centrality metrics are different for each algorithm at a meaningful level. In this work, we analyzed only four commonly used centrality metrics: degree centrality, betweenness centrality, closeness centrality, and eigenvector centrality. Eigenvector centrality records the lowest performance in all models except support vector machines. Closeness centrality and betweenness centrality show similar performance across all models. Degree centrality ranking moderate across overall models while betweenness centrality always ranking higher than degree centrality. Finally, closeness centrality is characterized by distinct differences in performance according to the model. It ranks first in logistic regression, artificial neural network, and decision tree withnumerically high performance. However, it only records very low rankings in support vector machine and K-neighborhood with low-performance levels. As the experiment results reveal, in a classification model, network centrality metrics over a subnetwork that connects the two nodes can effectively predict the connectivity between two nodes in a social network. Furthermore, each metric has a different performance depending on the classification model type. This result implies that choosing appropriate metrics for each algorithm can lead to achieving higher recommendation performance. In general, betweenness centrality can guarantee a high level of performance in any model. It would be possible to consider the introduction of proximity centrality to obtain higher performance for certain models.

• Journal of Gifted/Talented Education
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• v.26 no.1
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• pp.37-51
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• 2016

This study examined the friendship expectation that science-gifted and non-gifted elementary students perceived in gifted class and regular class. In 233 science-gifted elementary students and 329 non-gifted elementary students, we measured the friendship expectation that sub-domains were intimacy, ability similarity, and morality. The results of this study were as follows: First, according to the results of comparing the friendship expectations of science-gifted and non-gifted students at the regular class, there was statistically significant intergroup difference in the sub-domains of intimacy and morality, but there was no significant difference in the sub-domain of ability similarity. Second, according to the results of comparing the friendship expectations of science-gifted at the gifted class and at the regular class, there was statistically significant difference in the sub-domain of intimacy, but there was no significant difference in the sub-domains of morality and ability similarity. Based the results, the implications to understand the friendship of the science gifted elementary students were suggested in depth.

• Journal of Intelligence and Information Systems
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• v.13 no.1
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• pp.91-105
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• 2007

This research suggests a recommendation system that enables bidirectional communications between the user and system using a utility range-based product recommendation algorithm in order to provide more dynamic and personalized recommendations. The main idea of the proposed algorithm is to find the utility ranges of products based on user specified preference information and calculate the similarity by using overlapping probability of two range values. Based on the probability, we determine what products are similar to each other among the products in the product list of collaborative companies. We have also developed a Web-based application system to recommend similar products to the customer. Using the system, we carry out the experiments for the performance evaluation of the procedure. The experimental study shows that the utility range-based approach is a viable solution to the similar product recommendation problems from the viewpoint of both accuracy and satisfaction rate.

• 한국HCI학회:학술대회논문집
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• 2008.02b
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• pp.30-35
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• 2008

The purpose of this study is to analyze and define user's emotional satisfaction factors to the PC OS GUI image. The study is to investigate the relationship between PC OS GUI Image and Sensitive Vocabula교 based on user's emotional preference. 47 user preferred sensitive words are collected by the initial survey. Through the similarity test, 47 words are narrowed down to 20 comprehend words. The semantic differential methods is used in the final survey with 5 step questionnaire. From this process, user preferred the GUI design that is vocabularized as Clear, Easy, Safety, Stability. Additionally, the result shows that the image of Clear is related to Safety and the image of Easy is related to Stability. The result of the study could be used in design PC OS GUI as base data.

• Journal of Intelligence and Information Systems
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• v.9 no.3
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• pp.117-135
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• 2003

This study is to propose a procedure solving scale problem of traditional collaborative filtering (CF) approach. The CF approach generally uses some similarity measures like correlation coefficient. So, as the user of the Website increases, the complexity of computation increases exponentially. To solve the scale problem, this study suggests a clustering model-based approach using Self-Organizing Map (SOM) and RFM (Recency, Frequency, Momentary) method. SOM clusters users into some user groups. The preference score of each item in a group is computed using RFM method. The items are sorted and stored in their preference score order. If an active user logins in the system, SOM determines a user group according to the user's characteristics. And the system recommends items to the user using the stored information for the group. If the user evaluates the recommended items, the system determines whether it will be updated or not. Experimental results applied to MovieLens dataset show that the proposed method outperforms than the traditional CF method comparatively in the recommendation performance and the computation complexity.

• Science of Emotion and Sensibility
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• v.5 no.1
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• pp.61-70
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• 2002

The purpose of this study was to develop 'Emotion Rating Scale induced by Odors'and to identify the structure of odor emotion induced by odors. At first 37 adjectives that describe odor to develop a rating scale were selected. Subjects were to rate odor emotion on a 7-point bipolar scale. 304 subjects participated and were as a group instructed to rate odor emotion. 53 out of 304 subjects were retested to test for reliability of the scale two weeks after under the same condition and finally 25 adjectives were then selected based on high test-retest reliability and factor loading, high contributing to one factor. 24 subjects each in 10s, 20s, 30s & 40s were to rate odor emotion induced by 5 different odors on the scale developed. The structure of odor emotion consisted of 'Esthetics', 'Intensity', 'Romance', 'Nature'and 'Character'. The structure of odor emotion by age appeared quite similar but that by different odors was little bit different.