• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.4
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• pp.9-15
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• 2023

Existing simple statistics-based recommendation systems rely solely on students' course enrollment history data, making it difficult to identify classes that match students' preferences. To address this issue, this study proposes a personalized major subject recommendation system based on deep reinforcement learning (DRL). This system gauges the similarity between students based on structured data, such as the student's department, grade level, and course history. Based on this information, it recommends the most suitable major subjects by comprehensively considering information about each available major subject and evaluations of the student's courses. We confirmed that this DRL-based recommendation system provides useful insights for university students while selecting their major subjects, and our simulation results indicate that it outperforms conventional statistics-based recommendation systems by approximately 20%. In light of these results, we propose a new system that offers personalized subject recommendations by incorporating students' course evaluations. This system is expected to assist students significantly in finding major subjects that align with their preferences and academic goals.

• Journal of KIISE:Software and Applications
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• v.31 no.4
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• pp.457-466
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• 2004

Recommendation is to offer information which fits user's interests and tastes to provide better services and to reduce information overload. It recently draws attention upon Internet users and information providers. The collaborative filtering is one of the widely used methods for recommendation. It recommends an item to a user based on the reference users' preferences for the target item or the target user's preferences for the reference items. In this paper, we propose a neural network based collaborative filtering method. Our method builds a model by learning correlation between users or items using a multi-layer perceptron. We also investigate integration of diverse information to solve the sparsity problem and selecting the reference users or items based on similarity to improve performance. We finally demonstrate that our method outperforms the existing methods through experiments using the EachMovie data.

• Journal of KIISE:Software and Applications
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• v.29 no.9
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• pp.669-675
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• 2002

In recent years most of personalized recommender systems in electronic commerce utilize collaborative filtering algorithm in order to recommend more appropriate items. User-based collaborative filtering is based on the ratings of other users who have similar preferences to a user in order to predict the rating of an item that the user hasn't seen yet. This nay decrease the accuracy of prediction because the similarity between two users is computed with respect to the two users and only when an item has been rated by the users. In item-based collaborative filtering, the preference of an item is predicted based on the similarity between the item and each of other items that have rated by users. This method, however, uses the ratings of users who are not the neighbors of a user for computing the similarity between a pair of items. Hence item-based collaborative filtering may degrade the accuracy of a recommender system. In this paper, we present a new approach that a user's neighborhood is used when we compute the similarity between the items in traditional item-based collaborative filtering in order to compensate the weak points of the current item-based collaborative filtering and to improve the prediction accuracy. We empirically evaluate the accuracy of our approach to compare with several different collaborative filtering approaches using the EachMovie collaborative filtering data set. The experimental results show that our approach provides better quality in prediction and recommendation list than other collaborative filtering approaches.

• Journal of Science Education
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• v.47 no.2
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• pp.180-191
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• 2023

The purpose of this study was to investigate the preferred SSI topics of students and teachers in elementary, middle, and high schools. It analyzed the similarity of students' and teachers' preferred SSI topics by school level using the cosine similarity measure. A total of 566 students and 327 teachers from elementary, middle, and high schools participated in the study. Sixty topics were identified and listed in the areas of environment, science and technology, health and medicine, and other social issues based on the literature and SSI programs. Students and teachers were asked to select five of their favorite topics. The data was collected online using SurveyMonkey. The collected data was divided into six groups of students and teachers, and the frequency of topic selection was analyzed within each group. The topic preference similarity was analyzed by calculating vector values based on the frequency of the selected topics and measuring the cosine similarity between students, teachers, and teachers and students by school level. The results are as follows: First, the cosine similarity of SSI Preferred Topics between students' school-level cohorts was higher between middle and high school students (0.982) than between elementary and middle school students (0.651) or between elementary and high school students (0.662). Second, the cosine similarity of SSI Preferred Topics between teachers' school-level cohorts was similar for all comparison groups between elementary, middle, and high school. Third, the SSI topic preference similarity between students and teachers by school level had a higher cosine similarity between the elementary student and teacher cohorts (0.974) than the other school level comparisons, middle school (0.621) or high school (0.645). Access to topics of interest to students in SSI education is strongly associated with motivation and persistence in learning, as well as an enjoyable learning experience and positive attitudes toward learning. Therefore, when designing SSI lessons, it is important to examine topics from the perspective of student interest, especially if the teacher has selected SSI topics that are different from students' preferences. Careful instructional design will be needed to overcome the gap.

• Journal of the Korean Society of Costume
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• v.43
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• pp.187-202
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• 1999

The objectives of this study were to analyze the effect of dress(Korean traditional dress and suit) of elderly Women and situation on impression formation. The experimental design was $10\times{2}\times{2(dress}\times{perceiver's age}\times{situation)}$ factorial design by 3 independent variables. The stimuli of color photographs of female in her 60's model and the semantic differential scale were used. Six variables of impression formation were used: preference: elegance: potency: activity: feminine: and modernity. Samples were 400 women 200 were in their twenties and 200 in their forties and fifties. The data were analyzed by $\alpha$-reliability t-test ANOVA and duncan's multiple range test. The Korean traditional dress with the combination of Korean traditional color(light blue upper dress with dark red purple collar and string.dark blue skit) had the most positive effect on impression of elegance. Pink traditional dress and light blue traditional dress had a negative effect on impression of potency activity and modernity. Red purple suit had a positive effect on potency and modernity. The interaction between dress perceiver's age and stituation was significant for the impression of activity. Women in their 40's and 50's perceived the activity of red purple suit positively in the situation of alumnae meeting more than in the wedding ceremony. The perceived age of the stimulus person was different according to dresses. Traditional dresses was perceived older than suits were. Women in their 40's and 50's evaluated preferences of the dresses positively more than 20's did. This means that 40's and 50's feel similarity with the stimulus person more than 20's as the age of model was in their 60's The result supports the theory that similarity is basic factor in interpersonal attraction.

• The Research Journal of the Costume Culture
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• v.25 no.6
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• pp.819-830
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• 2017

This study aims to gather precise information on the real fabric color and texture, and purchasing intention of mobile shoppers buying clothes. Eighty volunteers participated in the sensory test on three smartphones with four colors and two fabrics-smooth taffeta and hairy doeskin. This study carried out the posteriori test using the one-way ANOVA and Duncan test by SPSS21.0. In the analysis' results of color preference, there were no differences among the four colors of taffeta between the smartphones, but different preferences between the red and yellow doeskin exist. In the case of the Samsung phone, which has an immense color distortion, the red fabric has a low color preference. In contrast, on the Apple phone yellow fabric had the highest preference because of its brightness. The Apple phone also has the highest purchasing intention of yellow colored taffeta, which is similar to the color preference results, although the real fabric has the opposite result. For doeskin, the real red and blue colored fabric has the highest purchasing intention. The Samsung phone has the biggest color mismatch with the real fabric. It also has the lowest purchasing intention of red taffeta fabric, while the LG phone has the lowest purchasing intention of blue fabric. Using the paired comparison method of the similarity between 'real' fabrics and the mobile version of fabric colors has a low similarity on all four colors of taffeta and doeskin fabrics. Therefore it can be concluded that phones do not represent the 'real' fabric color.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39C no.8
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• pp.626-636
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• 2014

Existing mash-up services could not be offered in accordance with the purposes and preferences of all users because they are created by the service developers. Therefore some precedent studies, which enable for individual users to create their own mash-up services automatically, have been conducted. In order to create automatic mash-up services, it is important to find elements to distinguish the possibility of mash-up. The precedent studies determine the possibility of mash-up through comparison of the similarity between input/output parameter names in the REST-based Open API. Only using the similarity to distinguish the possibility of mash-up, however, some unintended mash-up results can be occurred because the property of input/output parameters are not considered. In this paper, we propose the method considering the properties of input/output parameters to decrease the unintended mash-up results and extend ontology proposed in precedent studies by applying this property. And we propose the algorithm to distinguish the possibility of mash-up using the expanded ontology and describe the result of automatic mash-up services.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.14 no.11
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• pp.5771-5777
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• 2013

The need of continuous management for diseases came to the fore as a chronic disease has increased, however, research related to personalized food intake analysis are insufficient. In diabetes risk analysis model of this study, food preferences are calculated by Pearson correlation coefficient that is proven method to assess the similarity, and diabetes risk is computed as a Logistic regression that was used in prevalence studies. For the Significance evaluation of this model, it was verified through t-test at 0.05 level of 52 comparison subjects and 52 control subjects. Both groups were significantly independent (p=0.046 <0.05). This model is a new way to personalized health management, through the application to healthcare system based on web and mobile.

• Journal of Information Processing Systems
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• v.12 no.4
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• pp.577-590
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• 2016

We studied the current state-of-the-art of Smart TV, the challenges and the drawbacks. Mainly we discussed the lack of end-to-end solution. We then illustrated the differences between Smart TV and IPTV from network service provider point of view. Unlike IPTV, viewer of Smart TV's over-the-top (OTT) services could be global, such as foreign nationals in a country or viewers having special viewing preferences. Those viewers are sparsely distributed. The existing TV service deployment models over Internet are not suitable for such viewers as they are based on content popularity, hence we propose a community based service deployment methodology with proactive content caching on rendezvous points (RPs). In our proposal, RPs are intermediate nodes responsible for caching routing and decision making. The viewer's community formation is based on geographical locations and similarity of their interests. The idea of using context information to do proactive caching is itself not new, but we combined this with "in network caching" mechanism of content centric network (CCN) architecture. We gauge the performance improvement achieved by a community model. The result shows that when the total numbers of requests are same; our model can have significantly better performance, especially for sparsely distributed communities.

• International Journal of Advanced Culture Technology
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• v.12 no.2
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• pp.205-214
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• 2024

This paper delves into the application of Gestalt psychology principles in logo design. It analyzes how these principles refine design elements to bolster the efficiency and impact of visual communication. Drawing from Gestalt psychology perspectives, the theoretical foundations and application methods of logo design simplification strategies are discussed. Through the analysis of Gestalt psychology effects in various types and styles of logo designs, this study compares the applicability and differences of logo design simplification strategies under different cultural and social contexts. Furthermore, it evaluates their role and value in enhancing the innovativeness and communicative impact of logo designs. The findings suggest that strategies informed by Gestalt psychology significantly improve the organization rules within logo designs, such as the relationship between figure and ground, proximity, similarity, and continuity. Thereby they enhance perceptual clarity, cognitive load, and aesthetic satisfaction. Moreover, these strategies promote creative thinking and problem-solving abilities in logo design. The results indicate that simplified design methods not only enhance aesthetic appeal but also improve the adaptability and recognizability of logos across different media and environments. This approach aligns with the minimalist and flat design trends of today's information age, meeting the evolving needs and aesthetic preferences of consumers.