• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.139-161
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• 2019

The utilization of the e-commerce market has become a common life style in today. It has become important part to know where and how to make reasonable purchases of good quality products for customers. This change in purchase psychology tends to make it difficult for customers to make purchasing decisions in vast amounts of information. In this case, the recommendation system has the effect of reducing the cost of information retrieval and improving the satisfaction by analyzing the purchasing behavior of the customer. Amazon and Netflix are considered to be the well-known examples of sales marketing using the recommendation system. In the case of Amazon, 60% of the recommendation is made by purchasing goods, and 35% of the sales increase was achieved. Netflix, on the other hand, found that 75% of movie recommendations were made using services. This personalization technique is considered to be one of the key strategies for one-to-one marketing that can be useful in online markets where salespeople do not exist. Recommendation techniques that are mainly used in recommendation systems today include collaborative filtering and content-based filtering. Furthermore, hybrid techniques and association rules that use these techniques in combination are also being used in various fields. Of these, collaborative filtering recommendation techniques are the most popular today. Collaborative filtering is a method of recommending products preferred by neighbors who have similar preferences or purchasing behavior, based on the assumption that users who have exhibited similar tendencies in purchasing or evaluating products in the past will have a similar tendency to other products. However, most of the existed systems are recommended only within the same category of products such as books and movies. This is because the recommendation system estimates the purchase satisfaction about new item which have never been bought yet using customer's purchase rating points of a similar commodity based on the transaction data. In addition, there is a problem about the reliability of purchase ratings used in the recommendation system. Reliability of customer purchase ratings is causing serious problems. In particular, 'Compensatory Review' refers to the intentional manipulation of a customer purchase rating by a company intervention. In fact, Amazon has been hard-pressed for these "compassionate reviews" since 2016 and has worked hard to reduce false information and increase credibility. The survey showed that the average rating for products with 'Compensated Review' was higher than those without 'Compensation Review'. And it turns out that 'Compensatory Review' is about 12 times less likely to give the lowest rating, and about 4 times less likely to leave a critical opinion. As such, customer purchase ratings are full of various noises. This problem is directly related to the performance of recommendation systems aimed at maximizing profits by attracting highly satisfied customers in most e-commerce transactions. In this study, we propose the possibility of using new indicators that can objectively substitute existing customer 's purchase ratings by using RFM multi-dimensional analysis technique to solve a series of problems. RFM multi-dimensional analysis technique is the most widely used analytical method in customer relationship management marketing(CRM), and is a data analysis method for selecting customers who are likely to purchase goods. As a result of verifying the actual purchase history data using the relevant index, the accuracy was as high as about 55%. This is a result of recommending a total of 4,386 different types of products that have never been bought before, thus the verification result means relatively high accuracy and utilization value. And this study suggests the possibility of general recommendation system that can be applied to various offline product data. If additional data is acquired in the future, the accuracy of the proposed recommendation system can be improved.

• Journal of Intelligence and Information Systems
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• v.21 no.4
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• pp.1-16
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• 2015

Traffic accident is one of the major cause of death worldwide for the last several decades. According to the statistics of world health organization, approximately 1.24 million deaths occurred on the world's roads in 2010. In order to reduce future traffic accident, multipronged approaches have been adopted including traffic regulations, injury-reducing technologies, driving training program and so on. Records on traffic accidents are generated and maintained for this purpose. To make these records meaningful and effective, it is necessary to analyze relationship between traffic accident and related factors including vehicle design, road design, weather, driver behavior etc. Insight derived from these analysis can be used for accident prevention approaches. Traffic accident data mining is an activity to find useful knowledges about such relationship that is not well-known and user may interested in it. Many studies about mining accident data have been reported over the past two decades. Most of studies mainly focused on predict risk of accident using accident related factors. Supervised learning methods like decision tree, logistic regression, k-nearest neighbor, neural network are used for these prediction. However, derived prediction model from these algorithms are too complex to understand for human itself because the main purpose of these algorithms are prediction, not explanation of the data. Some of studies use unsupervised clustering algorithm to dividing the data into several groups, but derived group itself is still not easy to understand for human, so it is necessary to do some additional analytic works. Rule based learning methods are adequate when we want to derive comprehensive form of knowledge about the target domain. It derives a set of if-then rules that represent relationship between the target feature with other features. Rules are fairly easy for human to understand its meaning therefore it can help provide insight and comprehensible results for human. Association rule learning methods and subgroup discovery methods are representing rule based learning methods for descriptive task. These two algorithms have been used in a wide range of area from transaction analysis, accident data analysis, detection of statistically significant patient risk groups, discovering key person in social communities and so on. We use both the association rule learning method and the subgroup discovery method to discover useful patterns from a traffic accident dataset consisting of many features including profile of driver, location of accident, types of accident, information of vehicle, violation of regulation and so on. The association rule learning method, which is one of the unsupervised learning methods, searches for frequent item sets from the data and translates them into rules. In contrast, the subgroup discovery method is a kind of supervised learning method that discovers rules of user specified concepts satisfying certain degree of generality and unusualness. Depending on what aspect of the data we are focusing our attention to, we may combine different multiple relevant features of interest to make a synthetic target feature, and give it to the rule learning algorithms. After a set of rules is derived, some postprocessing steps are taken to make the ruleset more compact and easier to understand by removing some uninteresting or redundant rules. We conducted a set of experiments of mining our traffic accident data in both unsupervised mode and supervised mode for comparison of these rule based learning algorithms. Experiments with the traffic accident data reveals that the association rule learning, in its pure unsupervised mode, can discover some hidden relationship among the features. Under supervised learning setting with combinatorial target feature, however, the subgroup discovery method finds good rules much more easily than the association rule learning method that requires a lot of efforts to tune the parameters.

• Journal of the Korean Institute of Landscape Architecture
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• v.47 no.3
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• pp.60-70
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• 2019

Today, Deoksugung Doldam-gil, which is a well-known area in Seoul, has become a mixed place as many places reaching a critical age have been converted into parks. However, the previous research on the Deoksugung Doldam-gil was deficient in that the user, an essential variable, was not considered when assessing the place. Based on that, this study aims to analyze and interpret the perception of the places in Deoksugung Doldam-gil and to analyze factors to further enrich the place to visitors. According to the research, the representative idea of Deoksugung Doldam-gil is "the distance you want to go" and that has influencing factors, such as vehicle restrictions and the improvement of the walking environment. The analysis of classifying the variables that make up the perception of the place, physical environments, activities and meanings showed high awareness in, "streets of green (3.95)" and "stone walls of curves (3.88)." In the category of activities, "walking activities in the inner city (4.01)" and "love and romance (3.57)" were high. These results seem to reflect the spatial characteristics of the streets and the familiar image of the place were important. Five factors were extracted from the factor analysis to provide a more detailed understanding of the place perception, the correlation between each factor, and the place atmosphere of Deoksugung Doldam-gil. These factors confirmed a high correlation between 'green landscape' and 'historicity.' This can be attributed to the fact that the analysis reflects vital space, visual experience, and free walking conditions to be important, and these variables are present in urban parks. It also indicates the long-accumulated image and behavior near the site of Deoksugung Palace, including the historical and cultural heritage. It was confirmed that the factors related to the cognitive perception of Deoksugung Doldam-gil and the formation of the atmosphere of the place were strongly recognized. It found that there was a need to reflect the value and importance of 'green' in the future as culture or in the use of preservation and management related to heritage. This study presented a direction to be noted from the perspective of a user's place awareness, but considered only a fraction of the variables that affect the multidimensional sense of place and location recognition, and thus must be supplemented in the future.

• Journal of Intelligence and Information Systems
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• v.21 no.4
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• pp.93-110
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• 2015

The recommender system is a system which recommends products to the customers who are likely to be interested in. Based on automated information filtering technology, various recommender systems have been developed. Collaborative filtering (CF), one of the most successful recommendation algorithms, has been applied in a number of different domains such as recommending Web pages, books, movies, music and products. But, it has been known that CF has a critical shortcoming. CF finds neighbors whose preferences are like those of the target customer and recommends products those customers have most liked. Thus, CF works properly only when there's a sufficient number of ratings on common product from customers. When there's a shortage of customer ratings, CF makes the formation of a neighborhood inaccurate, thereby resulting in poor recommendations. To improve the performance of CF based recommender systems, most of the related studies have been focused on the development of novel algorithms under the assumption of using a single profile, which is created from user's rating information for items, purchase transactions, or Web access logs. With the advent of big data, companies got to collect more data and to use a variety of information with big size. So, many companies recognize it very importantly to utilize big data because it makes companies to improve their competitiveness and to create new value. In particular, on the rise is the issue of utilizing personal big data in the recommender system. It is why personal big data facilitate more accurate identification of the preferences or behaviors of users. The proposed recommendation methodology is as follows: First, multimodal user profiles are created from personal big data in order to grasp the preferences and behavior of users from various viewpoints. We derive five user profiles based on the personal information such as rating, site preference, demographic, Internet usage, and topic in text. Next, the similarity between users is calculated based on the profiles and then neighbors of users are found from the results. One of three ensemble approaches is applied to calculate the similarity. Each ensemble approach uses the similarity of combined profile, the average similarity of each profile, and the weighted average similarity of each profile, respectively. Finally, the products that people among the neighborhood prefer most to are recommended to the target users. For the experiments, we used the demographic data and a very large volume of Web log transaction for 5,000 panel users of a company that is specialized to analyzing ranks of Web sites. R and SAS E-miner was used to implement the proposed recommender system and to conduct the topic analysis using the keyword search, respectively. To evaluate the recommendation performance, we used 60% of data for training and 40% of data for test. The 5-fold cross validation was also conducted to enhance the reliability of our experiments. A widely used combination metric called F1 metric that gives equal weight to both recall and precision was employed for our evaluation. As the results of evaluation, the proposed methodology achieved the significant improvement over the single profile based CF algorithm. In particular, the ensemble approach using weighted average similarity shows the highest performance. That is, the rate of improvement in F1 is 16.9 percent for the ensemble approach using weighted average similarity and 8.1 percent for the ensemble approach using average similarity of each profile. From these results, we conclude that the multimodal profile ensemble approach is a viable solution to the problems encountered when there's a shortage of customer ratings. This study has significance in suggesting what kind of information could we use to create profile in the environment of big data and how could we combine and utilize them effectively. However, our methodology should be further studied to consider for its real-world application. We need to compare the differences in recommendation accuracy by applying the proposed method to different recommendation algorithms and then to identify which combination of them would show the best performance.

• Journal of the Korean Institute of Landscape Architecture
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• v.43 no.4
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• pp.75-86
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• 2015

The survey was taken from July to August 2012 by users who visited Gyeongju National Park to compare the perceived gap of users regarding management policy by characteristic of Gyeongju National Park district type in this study. Gyeongju National Park users' characteristic, use pattern and perception regarding park management policy were created as survey items. First, district type was classified based on use pattern of the visitor and the key resources of 8 districts in Gyeongju National Park. Tohamsan District, which has many visitors for the purpose of scenery appreciation and recreation with Bulguksa and Seokguram Grotto, is classified as tourism type, Namsan and Daebon District, which bring in many visitors seeking to learn about historical culture and environmental education, could be classified as historical culture education types, and Hwarang, Seoak, Sogeum River, Gumisan District are places residents use for physical training, hiking and walking to improve health, thus classifying them as neighborhood park types. People perceived that the tourism type is where users for historical artifact tours are concentrated, thus consideration for plans that can improve visitors' satisfaction from a user limit policy is required, and a manager's right to control use behavior must be reinforced in historical culture education types. On the other hand, users of neighborhood parks found the lowest necessity for most of the policy, and this showed that users of each of Gyeongju National Park's districts felt differently about the need for policies. This result is expected to be utilized as a database for introducing policy that reflects the perception of users in each districts of Gyeongju National Park in the future.

• Korea Science and Art Forum
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• v.37 no.4
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• pp.297-308
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• 2019

Air pollution due to the fine dust level updating every day, and the problem of indoor air pollution due to ventilation difficulties and indoor discharge pollutants is also serious. In order to improve the indoor air quality, the air purification effect using the plants is prominent. In this study was started to investigated the living environment of modern people, the risk of indoor air pollution and the improvement function of plants, and to activate plant application. The purpose of this study is to analyze the main functions and design status of domestic and overseas plant - related applications, and to understand the actual use of modern plant applications and to help them learn more convenient plant - related knowledge. Therefore, this paper attempted to establish a basis for suggesting a new plant application by conducting a survey on the health effects of indoor air pollution and user awareness of plant - related applications. The results and contents of the study are as follows. First, as a theoretical review, indoor air pollution is more dangerous to modern people who have a high proportion of indoor living time and adversely affects their health. In order to solve such a problem, it has been shown that air conditioning and stress reduction can be effectively achieved by placing plants in the indoor space. Second, the analysis of the previous study shows the risk of indoor air pollution and its adverse effects on health. In addition, I have been able to find some researches related to the improvement of the indoor air by using the air purifying plants, and I can see the improvement of the user's behavior through the development or improvement of the application. Third, as a result of the survey on the status of domestic and overseas plant application, the main function of the application having high installation number was watering notification, provision of basic information of plants, and most of the functions were plant discerment through cameras. Fourth, most of the survey respondents have either raised or raised plants. Those who have little experience with plant applications have also shown positive feedback in the future on the use of plant-related applications. In addition, due to social problems such as air pollution, air purification using plants and functional plants showed high interest. Based on these results, we propose the need for a multi-functional plant application that can improve the indoor air pollution and facilitate the provision of information related to it.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.8 no.2
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• pp.107-117
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• 2013

The main objective of this study lies at examining economic features of IT security investment and comparing alternative mechanisms to achieve optimal provision of IT security resources within a firm. There exists a paucity of economic analysis that provide useful guidelines for making critical decisions regarding the optimal level of provision of IT security and how to share the costs among different users within a firm. As a preliminary study, this study first argues that IT security resources share some unique characteristics of pure public goods, namely nonrivalry of consumption and nonexcludability of benefit. IT security provision problem also suffers from information asymmetry problem with regard to the valuation of an individual user for IT security goods. Then, through an analytical framework, it is shown that the efficient provision condition at the overall firm level is not necessarily satisfied by individual utility maximizing behavior. That is, an individual provision results in a suboptimal solution, especially an underprovision of the IT security good. This problem is mainly due to the nonexcludability property of pure public goods, and is also known as a free-riding problem. The fundamental problem of collective decision-making is to design mechanisms that both induce the revelation of the true information and choose an 'optimal' level of the IT security good within this framework of information asymmetry. This study examines and compares three alternative demand-revealing mechanisms within the IT security resource provision context, namely the Clarke-Groves mechanism, the expected utility maximizing mechanism and the Groves-Ledyard mechanism. The main features of each mechanism are discussed along with its strengths, weaknesses, and different applicability in practice. Finally, the limitations of the study and future research are discussed.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.48 no.10
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• pp.773-782
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• 2020

The development of joint FE models for deep learning neural network (DLNN)-based hybrid FEA is presented. Material models of bolts and bearings in the front axle of tractor, showing complex behavior induced by various tightening conditions, were replaced with DLNN models. Bolts are modeled as one-dimensional Timoshenko beam elements with six degrees of freedom, and bearings as three-dimensional solid elements. Stress-strain data were extracted from all elements after finite element analysis subjected to various load conditions, and DLNN for bolts and bearing were trained with Tensorflow. The DLNN-based joint models were implemented in the ABAQUS user subroutines where stresses from the next increment are updated and the algorithmic tangent stiffness matrix is calculated. Generalization of the trained DLNN in the FE model was verified by subjecting it to a new loading condition. Finally, the DLNN-based FEA for the front axle of the tractor was conducted and the feasibility was verified by comparing with results of a static structural experiment of the actual tractor.

• Journal of Digital Convergence
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• v.15 no.5
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• pp.381-390
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• 2017

Lifestyle is an important variable to understand consumer behavior because it is a lifestyle that is common to all members of society or some members of society. so In this study, the survey has been performed on adult mobile game users over the age of 20 in Korea and China to evaluate the correlation between lifestyle and will of continuous use of game by mobile game users, and the hierarchical multiple regression analysis has been performed on the collected data by using the statistical package program SPSS 20.0. There were total 212 respondents with the gender ratio of 50:50, and 107 respondents were Korean, and 105 respondents were Chinese. As a result of study, first, regardless of lifestyle, more male and more Chinese respondents showed higher will of continuous use. Second, among mobile game user lifestyles, the challenge-oriented, trend-oriented, conservative, and ostentatious lifestyles could become significant causal variables for the will of continuous use of game. Yet, the influence of such lifestyle was not correlated with individual genders or nationalities. The result of this study will be provided as the basic data to establish the strategic solution for companies and government policies for entrance into the mobile game market of China in the future.

• Journal of Korean society of Dental Hygiene
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• v.9 no.3
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• pp.493-506
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• 2009

The purpose of this study was to examine the awareness of people in general characteristics about oral malodor. The subjects in this study are 184 people who visited the clinical practice lab at J health college to get their teeth scaled. After conducting a survey from May 1 to June 3, 2008, we selected four different ares and then analyzed the answer sheets from 179 respondents including smoking/nonsmoking, scaling experience, toothbrushing frequency and the use of oral hygiene supplies. SPSS WIN 12.0 program was used to make a frequency analysis and cross analysis. The findings of the study are as follows: 1. Concerning an intention of treatment for oral malodor, 37.4% didn't intend to receive treatment even in case of having bad breath. 28.5% didn't yet have any definite idea about that, and 20.7% had no mind to do that at all. 10.6% had an intention to receive treatment, and 2.8% want to receive treatment. 2. As for how to cope with oral malodor in case of suffering from it, 47.5% chewed gums or ate candy. 25.1% scarcely care about that, and 15.6% covered their mouth whenever they spoke. 9.5% had little confidence about talking to others, and 2.2% found it difficult to build an amicable interpersonal relationship. 3. Concerning what to do about another person's oral malodor, 40.8% did nothing, and 19% talked to the person about that. 17.3% gave him or her chewing gum. Among their oral health characteristics, toothbrushing frequency made a significant difference to the way they responded to another person's oral malodor(p<.05). 4. As to subjective feelings about another person's oral malodor, 41.9% just found it bearable. 36.9% were a little displeased, and 9.5% never felt bad about another person's bad breath. 8.9% tried to avoid the person, and 2.8% advised him or her to chew gum. 5. Regarding an intention of participating in a oral malodor program, 46.9% had no idea about that. 31.3% intended to participate in the program, and 13.4% wanted to do that without fail. 6.1% had no mind for that, and 2.2% were never going to do that. Among characteristics of the user oral hygiene device made a significant difference whether to participating in the oral malodor program(p<.05).