• 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.

• The Journal of Internal Korean Medicine
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• v.31 no.4
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• pp.792-812
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• 2010

In the 18th century, Xu Lingtai (徐靈胎) and Yoshimasu Todo (吉益東洞) were famous doctors advocating ancient medicine, though they lived in different countries, China and Japan. We compared their major books, analyzed their academic thoughts and then took conclusions as below. 1. The first, for instance "Classified Prescriptions of Treatise on Cold Damage Diseases, 傷寒論類方" and "Classified Assemblage of Prescriptions, 類聚方". Based on essential thought that a prescription and a syndrome should correspond, these books arranged and classified the Zhang Zhongjing (張仲景)'s texts."Classified Prescriptions of Treatise on Cold Damage Diseases", based on the thought that principles, methods, formulas and medicinals (理法方藥) were integrated in prescriptions, tried to find out the implicit treatment rules in prescriptions and syndromes through analyzing "Treatise on Cold Damage Diseases, 傷寒論". On the other hand, because Classified Assemblage of Prescriptions focused on the syndromes of ancient prescriptions (古方), it classified and collected the related texts of Treatise on Cold Damage Diseases and "Synopsis of Prescriptions of the Golden Chamber, 금궤요략", and then suggested only simple instructions on how to prescribe medicine. So in this book, the trend of experience was clear. 2. The second, there is "100 Kinds Records from Shennong's Classic of Materia Medica, 神農本草經百種錄" and "Description work of herbal pharmacology comprised of excerpts from Shanhanlun and medical experiences, 藥徵". Though both of these books are professional oriental pharmacology publications that advocate reactionism, there were remarkable differences in writing style between them. "Description work of herbal pharmacology comprised of excerpts from Shanhanlun and medical experiences" was based on "Treat on Cold Damage Diseases" and "Synopsis of Prescriptions of the Golden Chamber", just explained the effects of medications and discussed 'matter of course (所當然)', but not discussed 'the reason why (所以然)'. In explaining style of syndromes, it confirmed through research, and emphasized the inductive method. On the other hand, "100 Kinds Records from Shennong's Classic of Materia Medica based on "Shennong's Classic of Materia Medica, 神農本草經", explained the nature of medications and discussed 'the reason why (所以然)'. In explaining style of syndromes, it annotated and explained, and emphasized the process of reasoning. 3. The third, there is "Discuss the Headwaters of Medicine, 醫學源流論" and Severance of Medical evils, 醫斷". Aiming the then medical theories fallen in confused state, these books brought order out of chaos, clarified the categories of medical research, and emphasized the scientific method that could put theories into practice and verify them. The difference is that "Severance of Medical Evils" researched only macroscopic viewable clinical phenomena, and even denied the existence of names of diseases and etiological causes. Thus, it emphasized the accumulation of experiences, laid emphasis on "watching and realizing (目認)", and "understand and taking in (解悟)". Discuss the Headwaters of Medicine extremely emphasized the research of 'something not occuring (未然)', that is to say, induced notions of a disease from observing clinical phenomena, furthermore based on these, predicted the 'something not occuring (未然)' and emphasized researching 'the reason why (所以然)'. As regards how they deal with the traditional theories and post-Zhang Zhongjing's medicines, "Severance of Medical evils" took completely denying attitudes. In case of "Discuss the Headwaters of Medicine", it could be used reasonably through specific situation and detailed analysis. Collectively speaking, there were some differences between medical theories of Xu Lingtai and Yoshimasu Todo. Actually, these differences were whether he tried to research the essence of disease, whether he tried to consider it rationally, and how he treated various opinions occurring in the theories of traditional medicine and clinical experience.

• Journal of Intelligence and Information Systems
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• v.17 no.4
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• pp.305-316
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• 2011

Recommender system is the system which, by using automated information filtering technology, recommends products or services to the customers who are likely to be interested in. Those systems are widely used in many different Web retailers such as Amazon.com, Netfix.com, and CDNow.com. Various recommender systems have been developed. Among them, Collaborative Filtering (CF) has been known as the most successful and commonly used approach. CF identifies customers whose tastes are similar to those of a given customer, and recommends items those customers have liked in the past. Numerous CF algorithms have been developed to increase the performance of recommender systems. However, the relative performances of CF algorithms are known to be domain and data dependent. It is very time-consuming and expensive to implement and launce a CF recommender system, and also the system unsuited for the given domain provides customers with poor quality recommendations that make them easily annoyed. Therefore, predicting in advance whether the performance of CF recommender system is acceptable or not is practically important and needed. In this study, we propose a decision making guideline which helps decide whether CF is adoptable for a given application with certain transaction data characteristics. Several previous studies reported that sparsity, gray sheep, cold-start, coverage, and serendipity could affect the performance of CF, but the theoretical and empirical justification of such factors is lacking. Recently there are many studies paying attention to Social Network Analysis (SNA) as a method to analyze social relationships among people. SNA is a method to measure and visualize the linkage structure and status focusing on interaction among objects within communication group. CF analyzes the similarity among previous ratings or purchases of each customer, finds the relationships among the customers who have similarities, and then uses the relationships for recommendations. Thus CF can be modeled as a social network in which customers are nodes and purchase relationships between customers are links. Under the assumption that SNA could facilitate an exploration of the topological properties of the network structure that are implicit in transaction data for CF recommendations, we focus on density, clustering coefficient, and centralization which are ones of the most commonly used measures to capture topological properties of the social network structure. While network density, expressed as a proportion of the maximum possible number of links, captures the density of the whole network, the clustering coefficient captures the degree to which the overall network contains localized pockets of dense connectivity. Centralization reflects the extent to which connections are concentrated in a small number of nodes rather than distributed equally among all nodes. We explore how these SNA measures affect the performance of CF performance and how they interact to each other. Our experiments used sales transaction data from H department store, one of the well?known department stores in Korea. Total 396 data set were sampled to construct various types of social networks. The dependant variable measuring process consists of three steps; analysis of customer similarities, construction of a social network, and analysis of social network patterns. We used UCINET 6.0 for SNA. The experiments conducted the 3-way ANOVA which employs three SNA measures as dependant variables, and the recommendation accuracy measured by F1-measure as an independent variable. The experiments report that 1) each of three SNA measures affects the recommendation accuracy, 2) the density's effect to the performance overrides those of clustering coefficient and centralization (i.e., CF adoption is not a good decision if the density is low), and 3) however though the density is low, the performance of CF is comparatively good when the clustering coefficient is low. We expect that these experiment results help firms decide whether CF recommender system is adoptable for their business domain with certain transaction data characteristics.

• Journal of Intelligence and Information Systems
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• v.17 no.4
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• pp.157-173
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• 2011

As the Internet use explodes recently, the malicious attacks and hacking for a system connected to network occur frequently. This means the fatal damage can be caused by these intrusions in the government agency, public office, and company operating various systems. For such reasons, there are growing interests and demand about the intrusion detection systems (IDS)-the security systems for detecting, identifying and responding to unauthorized or abnormal activities appropriately. The intrusion detection models that have been applied in conventional IDS are generally designed by modeling the experts' implicit knowledge on the network intrusions or the hackers' abnormal behaviors. These kinds of intrusion detection models perform well under the normal situations. However, they show poor performance when they meet a new or unknown pattern of the network attacks. For this reason, several recent studies try to adopt various artificial intelligence techniques, which can proactively respond to the unknown threats. Especially, artificial neural networks (ANNs) have popularly been applied in the prior studies because of its superior prediction accuracy. However, ANNs have some intrinsic limitations such as the risk of overfitting, the requirement of the large sample size, and the lack of understanding the prediction process (i.e. black box theory). As a result, the most recent studies on IDS have started to adopt support vector machine (SVM), the classification technique that is more stable and powerful compared to ANNs. SVM is known as a relatively high predictive power and generalization capability. Under this background, this study proposes a novel intelligent intrusion detection model that uses SVM as the classification model in order to improve the predictive ability of IDS. Also, our model is designed to consider the asymmetric error cost by optimizing the classification threshold. Generally, there are two common forms of errors in intrusion detection. The first error type is the False-Positive Error (FPE). In the case of FPE, the wrong judgment on it may result in the unnecessary fixation. The second error type is the False-Negative Error (FNE) that mainly misjudges the malware of the program as normal. Compared to FPE, FNE is more fatal. Thus, when considering total cost of misclassification in IDS, it is more reasonable to assign heavier weights on FNE rather than FPE. Therefore, we designed our proposed intrusion detection model to optimize the classification threshold in order to minimize the total misclassification cost. In this case, conventional SVM cannot be applied because it is designed to generate discrete output (i.e. a class). To resolve this problem, we used the revised SVM technique proposed by Platt(2000), which is able to generate the probability estimate. To validate the practical applicability of our model, we applied it to the real-world dataset for network intrusion detection. The experimental dataset was collected from the IDS sensor of an official institution in Korea from January to June 2010. We collected 15,000 log data in total, and selected 1,000 samples from them by using random sampling method. In addition, the SVM model was compared with the logistic regression (LOGIT), decision trees (DT), and ANN to confirm the superiority of the proposed model. LOGIT and DT was experimented using PASW Statistics v18.0, and ANN was experimented using Neuroshell 4.0. For SVM, LIBSVM v2.90-a freeware for training SVM classifier-was used. Empirical results showed that our proposed model based on SVM outperformed all the other comparative models in detecting network intrusions from the accuracy perspective. They also showed that our model reduced the total misclassification cost compared to the ANN-based intrusion detection model. As a result, it is expected that the intrusion detection model proposed in this paper would not only enhance the performance of IDS, but also lead to better management of FNE.

• Journal of Intelligence and Information Systems
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• v.18 no.1
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• pp.125-141
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• 2012

These days, the malicious attacks and hacks on the networked systems are dramatically increasing, and the patterns of them are changing rapidly. Consequently, it becomes more important to appropriately handle these malicious attacks and hacks, and there exist sufficient interests and demand in effective network security systems just like intrusion detection systems. Intrusion detection systems are the network security systems for detecting, identifying and responding to unauthorized or abnormal activities appropriately. Conventional intrusion detection systems have generally been designed using the experts' implicit knowledge on the network intrusions or the hackers' abnormal behaviors. However, they cannot handle new or unknown patterns of the network attacks, although they perform very well under the normal situation. As a result, recent studies on intrusion detection systems use artificial intelligence techniques, which can proactively respond to the unknown threats. For a long time, researchers have adopted and tested various kinds of artificial intelligence techniques such as artificial neural networks, decision trees, and support vector machines to detect intrusions on the network. However, most of them have just applied these techniques singularly, even though combining the techniques may lead to better detection. With this reason, we propose a new integrated model for intrusion detection. Our model is designed to combine prediction results of four different binary classification models-logistic regression (LOGIT), decision trees (DT), artificial neural networks (ANN), and support vector machines (SVM), which may be complementary to each other. As a tool for finding optimal combining weights, genetic algorithms (GA) are used. Our proposed model is designed to be built in two steps. At the first step, the optimal integration model whose prediction error (i.e. erroneous classification rate) is the least is generated. After that, in the second step, it explores the optimal classification threshold for determining intrusions, which minimizes the total misclassification cost. To calculate the total misclassification cost of intrusion detection system, we need to understand its asymmetric error cost scheme. Generally, there are two common forms of errors in intrusion detection. The first error type is the False-Positive Error (FPE). In the case of FPE, the wrong judgment on it may result in the unnecessary fixation. The second error type is the False-Negative Error (FNE) that mainly misjudges the malware of the program as normal. Compared to FPE, FNE is more fatal. Thus, total misclassification cost is more affected by FNE rather than FPE. To validate the practical applicability of our model, we applied it to the real-world dataset for network intrusion detection. The experimental dataset was collected from the IDS sensor of an official institution in Korea from January to June 2010. We collected 15,000 log data in total, and selected 10,000 samples from them by using random sampling method. Also, we compared the results from our model with the results from single techniques to confirm the superiority of the proposed model. LOGIT and DT was experimented using PASW Statistics v18.0, and ANN was experimented using Neuroshell R4.0. For SVM, LIBSVM v2.90-a freeware for training SVM classifier-was used. Empirical results showed that our proposed model based on GA outperformed all the other comparative models in detecting network intrusions from the accuracy perspective. They also showed that the proposed model outperformed all the other comparative models in the total misclassification cost perspective. Consequently, it is expected that our study may contribute to build cost-effective intelligent intrusion detection systems.

• Korean Journal of School Psychology
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• v.16 no.3
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• pp.231-263
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• 2019

The purpose of this study was to verify the effects of growth orientation, life satisfaction, perceived stress, positive psychological capital and interpersonal relationships in the group coaching program for the promotion of growth orientation for university students based on the model of the social-cognitive approach to motivation. The program consisted of eight topics: growth orientation, growth mindset and brain plasticity, self-directed goal setting, talent which is a product of ongoing effort, failure attitude and perspective change, positive emotion, thinking and behavior, value of growth orientation and self-coaching, respectively. The program comprised a total of eight sessions, 120 minutes each, and the final program was completed through a preliminary experiment with three university students. In order to verify the effectiveness of the program, 48 university students were divided into 16 in the experimental group, 16 in the comparative group, and 16 in the control group. The experimental group participated in the group coaching program to enhance the growth orientation based on the model of the social-cognitive approach to motivation developed in this study, the comparative group participated in a learning goal orientation improvement program based on an incremental implicit theory, and the control group did not carry out any program. Three groups were tested in pre, post, follow-up1(after 1 month) and follow-up2(after 3 months) in order to growth orientation, life satisfaction, perceived stress, positive psychological capital and interpersonal relationships. We performed analysis to confirm the homogeneity to the data of the three groups and to verify the interaction effects between times and groups. As a result, it was confirmed that the group coaching program to promote growth orientation, life satisfaction, perceived stress, positive psychological capital and interpersonal relationships had statistically significant effect and was more effective than the comparative program due to the larger effective size. Also, we confirmed that the coaching effect was sustained after the program was finished and more effectively maintained than the comparative program. Based on the results of this study, this study has academic implications because it verify the effectiveness of the group coaching for the promotion of the growth orientation by scient ic method.