• 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.

• 대한공업교육학회지
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• v.33 no.2
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• pp.192-217
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• 2008

This paper investigated the perceived criticalities and patterns of Korean Professional Engineer's competency regarding the working activities of automative product development, manufacturing, etc by using questionnaires responded to the survey which were applied to the automotive professors, experts and professional engineers (vocational parties) by e/mail, etc. This research investigated the following questions: First, what are the characteristic patterns, relevancy and perceived criticalities of Korean Professional Engineer's competencies? Second, What are the ranked priority of the Korean Professional Engineers' competencies? Are there any differency for each item, sub group of job, intelectual criterior of the competencies between relevancy and perceived criticalities according to the types of vocational parties, etc.? Accoring to the results; first, Professor group showed highest points among 3 groups per each item of the competencies by vocational parties Second, Chassis design group ranked top position among the 8 sub groups by vocational parties and, third, Problem Solving Knowledge ranked highest points than any others. Korean Professional Engineers are found to be positioned as key members, leaders and managers on surveying market, product planning, designing product & components, developing component parts, establishing shop with production equipment, managing quality control & material handling, organizing relevant meetings, developing human resources by training and learning, to back up finance with law matters, cooperating with concerned parties to achieve organizational goals, and to coordinate projects. etc, identifying ethical issues and business skills in order to survive and win to be competitive in various kinds of the automotive industry battle fields.