• Journal of Intelligence and Information Systems
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• v.26 no.4
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• pp.67-86
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• 2020

Recommender Systems have been huge influence users and business more and more. Recently the importance of E-commerce has been reached rapid growth greatly in world-wide COVID-19 pandemic. Recommender system is the center of E-commerce lively. Top ranked E-commerce managers mentioned that recommender systems have a major influence on customer's purchase such as about 50% of Netflix, Amazon sales from their recommender systems. Most algorithms have been focused on improving accuracy of recommender system regardless of novelty, diversity, serendipity etc. Recommender systems with only high accuracy cannot satisfy business long-term profit because of generating sales polarization. In addition, customers do not experience enjoyment of shopping from only focusing accuracy recommender system because customer's preference is changed constantly. Therefore, recommender systems with various values need to be developed for user's high satisfaction. Reranking is the most useful methodology to realize diversity of recommender system. In this paper, diversity of recommender system is represented through constructing high similarity with users who have different preference using each user's purchased item's category algorithm. It is distinguished from past research approach which is changing the algorithm of recommender system without user's diversity preference level. We tried to discover user's diversity preference level and observed the results how the effect was different according to user's diversity preference level. In addition, graph-based recommender system was used to show diversity through user's network, not collaborative filtering. In this paper, Amazon Grocery and Gourmet Food data was used because the low-involvement product, such as habitual product, foods, low-priced goods etc., had high probability to show customer's diversity. First, a bipartite graph with users and items simultaneously is constructed to make graph-based recommender system. However, each users and items unipartite graph also need to be established to show diversity of recommender system. The weight of each unipartite graph has played crucial role changing Jaccard Distance of item's category. We can observe two important results from the user's unipartite network. First, the user's diversity preference level is observed from the network and second, dissimilar users can be discovered in the user's network. Through the research process, diversity of recommender system is presented highly with small accuracy loss and optimalization for higher accuracy is possible controlling diversity ratio. This paper has three important theoretical points. First, this research expands recommender system research for user's satisfaction with various values. Second, the graph-based recommender system is developed newly. Third, the evaluation indicator of diversity is made for diversity. In addition, recommender systems are useful for corporate profit practically and this paper has contribution on business closely. Above all, business long-term profit can be improved using recommender system with diversity and the recommender system can provide right service according to user's diversity level. Lastly, the corporate selling low-involvement products have great effect based on the results.

• Journal of the Ergonomics Society of Korea
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• v.28 no.4
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• pp.147-154
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• 2009

The tactical computer is currently being developed and installed in armored vehicles and tanks for reinforcement. With the tactical computer, Korea Army will be able to grasp the deployment status of our forces, enemy, and obstacles under varying situations. Furthermore, it makes the exchange of command and tactical intelligence possible. Recent studies showed that the task performance is greatly affected by the user interface. The U.S. Army is now conducting user-centered evaluation tests based on C2 (Command & Control) to develop tactical intelligence machinery and tools. This study aims to classify and regroup subordinate menu functions according to the user-centered task performance for the Korea Army's tactical computer. Also, the research suggests an ergonomically sound layout and size of main touch buttons by considering human factors guidelines for button design. To achieve this goal, eight hierarchical subordinate menu functions are initially drawn through clustering analysis and then each group of menu functions was renamed. Based on the suggested menu structure, new location and size of the buttons were tested in terms of response time, number of error, and subjective preference by comparing them to existing ones. The result showed that the best performance was obtained when the number of buttons or functions was eight to conduct tactical missions. Also, the improved button size and location were suggested through the experiment. It was found in addition that the location and size of the buttons had interactions regarding the user's preference.

• Journal of Digital Convergence
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• v.15 no.4
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• pp.461-468
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• 2017

The current available recommendation systems for mobile games have a couple of problems. First, there is no knowing whether they make a pattern recommendation for games that actual users prefer or for games that they are simply interested in. It is also impossible to know the subjective preference of users in a direct manner. An AHP(Analytic Hierarchy Process)-based recommendation system for mobile games was thus developed to reflect the subjective preference of users directly, but it had its own problem since the degree of preference could vary among users in spite of the same scale for their preferable items. In an effort to solve those problems, this study implemented a recommendation system for mobile games by applying triangular fuzzy numbers of the Fuzzy-AHP technique and the independence of evaluation items in the Bayesian Network. The findings show that the proposed recommendation system recorded the highest accuracy of recommendation results and the highest level of user satisfaction.

• Journal of the Korean Society for Railway
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• v.12 no.5
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• pp.707-713
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• 2009

Planning railferry system in Korea-China trade routes, the overall evaluation and customer response are coming under spotlight in a preference and feasibility perspective. Persistent Railferry issue has been an area of study for the last decade. But until now, a surveyor or the evaluation from the actual users were exceptionally rate. Consequently, there is a need for a more detailed study into the practical issues such as customer response and railferry demand etc. Using a questionnaire survey from actual users, this paper examines the underlying evaluation on the Railferry system both in terms of demand and preference. The research questions were analyzed on data collected from almost 150 users in Korea. A range of arguments and expected problems were summarized. The results in general find a negative relationship between railferry preference and respondents' work experience. Findings in the reasons respondents are objecting the new railferry system, the suitable cargo type of railferry, and the possible barriers to introduce the system are discussed. Nonetheless further researches into railferry system as a possible alternative for Korea-China-Japan corridor should be followed in the near future.

• The Korean Journal of Applied Statistics
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• v.35 no.2
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• pp.265-283
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• 2022

MovieLens data consists of recorded movie evaluations that was often used to measure the evaluation score in the recommendation system research field. In this paper, we provide additional information obtained by clustering user-specific genre preference information through movie evaluation data and movie genre data. Because the number of movie ratings per user is very low compared to the total number of movies, the missing rate in this data is very high. For this reason, there are limitations in applying the existing clustering methods. In this paper, we propose a convex clustering-based method using the pairwise fused penalty motivated by the analysis of MovieLens data. In particular, the proposed clustering method execute missing imputation, and at the same time uses movie evaluation and genre weights for each movie to cluster genre preference information possessed by each individual. We compute the proposed optimization using alternating direction method of multipliers algorithm. It is shown that the proposed clustering method is less sensitive to noise and outliers than the existing method through simulation and MovieLens data application.

• Journal of Korea Multimedia Society
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• v.24 no.12
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• pp.1693-1707
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• 2021

The purpose of this study was to improve the experience of visiting obstetrics and gynecology of single women. After analyzing previous studies on Korean single women's perception of visiting obstetrics and gynecology, Contextual Interviews and Cultural Probes were conducted on single women in their 20s who visited obstetrics and gynecology. Based on this, personas were constructed to solidify the direction of problem solving by identifying the behavioral patterns and characteristics of single women. In this study, factors that hinder unmarried women's visits to obstetrics and gynecology and improvement measures were derived based on the information obtained using service design tools such as User Journey Mapping and Stakeholders' Map. Afterwards, a preference survey was conducted to increase the persuasiveness of the proposed method. The follow-up research task is to produce and propose the derived solution as a prototype that can be used in the actual field, and then proceed with user evaluation.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.655-656
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• 2022

Among the analysis methods for a recommendation system, collaborative filtering is a major representative method in a recommendation system based on data analysis. A general usage method is a technique of finding a common pattern by using evaluation data of users for various items, and recommending a preferred item for a specific user. Therefore, in this paper, various algorithms were used to measure the index, and an algorithm suitable for prediction of user preference was found and presented.

• Journal of the Korean Institute of Landscape Architecture
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• v.46 no.4
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• pp.11-20
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• 2018

This paper deals with Post Occupancy Evaluation (POE) of 2.28 Memorial Park in Daegu. The park is located in center of the city. The results and discussions could be used as basic data for urban park planning and design, in CBD. A questionnaire was conducted for 15 days from May 11, to May 26, 2016, and a sample group consisting of 230 on site users. The behavior, satisfaction, and preference of space image, were surveyed. The behavior analysis, satisfaction, and image evaluation questionnaire were derived using previous studies, and reliability, factor analysis and multiple regression analysis were conducted using SPSS. As a result, the items were very reliable, causal factors were extracted, and the variables that affect satisfaction and image preference were able to be identified. In the 2.28 Memorial Park, user satisfaction and image evaluation value were very high in most items. As a result of the assessment, the green-water landscape factors and cleanness factors, which have the greatest influence on satisfaction and preference, should be continuously maintained. Due to the characteristics of the city center parks, the users of the parks also felt environmental problems in the urban areas, so the users assessed noise levels, plant species, and air quality at a low level. Therefore, to solve this, plans such as noise abatement and extension of green space should be ongoing.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.41 no.4
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• pp.131-137
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• 2018

People write reviews of numerous products or services on the Internet, in their blogs or community bulletin boards. These unstructured data contain important emotions and opinions about the author's product or service, which can provide important information for future product design or marketing. However, this text-based information cannot be evaluated quantitatively, and thus they are difficult to apply to mathematical models or optimization problems for product design and improvement. Therefore, this study proposes a method to quantitatively extract user's opinion or preference about a specific product or service by utilizing a lot of text-based information existing on the Internet or online. The extracted unstructured text information is decomposed into basic unit words, and positive rate is evaluated by using existing emotional dictionaries and additional lists proposed in this study. This can be a way to effectively utilize unstructured text data, which is being generated and stored in vast quantities, in product or service design. Finally, to verify the effectiveness of the proposed method, a case study was conducted using movie review data retrieved from a portal website. By comparing the positive rates calculated by the proposed framework with user ratings for movies, a guideline on text mining based evaluation of unstructured data is provided.

• Journal of the Korean Institute of Landscape Architecture
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• v.30 no.3
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• pp.64-72
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• 2002

The purpose of this paper is to examine the effects of interior landscape that influence preference at a department store in order to answer the research question; What are the effective factors of interior landscape that affect preference at a department store. After review of the effect of interior landscape, and the interior landscape at a department store, we constructed a literature framework and have formulated the hypothesis of this research. We have analyzed the data which surveyed 108 visitors about the interior landscape in a department store, using factor analysis, Pearson's correlation analysis, and the multiple linear regression method. We found that; 1) eleven variables can be selected for the effects of interior landscape at department store: accessibility, image, stay, distinction, comfort, complexity, cleanness, mystery, purification of atmosphere, noise and harmony. Among the 11 independent variables used to study the effect of interior landscape at a department store, the image and purification of atmosphere highly affect preference. 2) These 11 variables are grouped by factor analysis as effects of amenity, attractiveness and identity. 3) As a result of multiple regression analysis, independent variables influencing preference were proved statistically significant at one percent level. 4) Regarding their relative contribution of interior landscape effect at a department store, the effects of amenity was the most important and it showed a level of importance 1.4 times higher than the effect of identity, and 1.25 times higher than the effect of attractiveness. The research results suggest the need for guidelines for the creation of interior landscape at department stores. The approach and analysis method adopted by this research is highly useful for the evaluation of interior landscape criteria at a department store. It is recommended that more practical study on factors affecting user's preference be performed in the future.