• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.29-56
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• 2021

Recently, various types of products have been launched with the rapid growth of the e-commerce market. As a result, many users face information overload problems, which is time-consuming in the purchasing decision-making process. Therefore, the importance of a personalized recommendation service that can provide customized products and services to users is emerging. For example, global companies such as Netflix, Amazon, and Google have introduced personalized recommendation services to support users' purchasing decisions. Accordingly, the user's information search cost can reduce which can positively affect the company's sales increase. The existing personalized recommendation service research applied Collaborative Filtering (CF) technique predicts user preference mainly use quantified information. However, the recommendation performance may have decreased if only use quantitative information. To improve the problems of such existing studies, many studies using reviews to enhance recommendation performance. However, reviews contain factors that hinder purchasing decisions, such as advertising content, false comments, meaningless or irrelevant content. When providing recommendation service uses a review that includes these factors can lead to decrease recommendation performance. Therefore, we proposed a novel recommendation methodology through CNN-based review usefulness score prediction to improve these problems. The results show that the proposed methodology has better prediction performance than the recommendation method considering all existing preference ratings. In addition, the results suggest that can enhance the performance of traditional CF when the information on review usefulness reflects in the personalized recommendation service.

• Korean Journal of Food Science and Technology
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• v.49 no.6
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• pp.649-658
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• 2017

The potential of the ethyl acetate fraction from Pteridium aquilinum (EFPA) to improve the cognitive function in high-fat diet (HFD)-induced diabetic mice was investigated. EFPA-treatment resulted in a significant improvement in the spatial, learning, and memory abilities compared to the HFD group in behavioral tests, including the Y-maze, passive avoidance, and Morris water maze. The diabetic symptoms of the EFPA-treated groups, such as fasting glucose and glucose tolerance, were alleviated. The administration of EFPA reduced the acetylcholinesterase (AChE) activity and malondialdehyde (MDA) content in mice brains, but increased the acetylcholine (ACh) and superoxide dismutase (SOD) levels. Finally, kaempferol-3-o-glucoside, a major physiological component of EFPA, was identified by using high-performance liquid chromatography coupled with a hybrid triple quadrupole-linear ion trap mass spectrometer (QTRAP LC-MS/MS).

• 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 the Korean earth science society
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• v.36 no.3
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• pp.258-279
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• 2015

The purpose of this study was to analyze the change of secondary earth science teachers' knowledge about the currents of the East Sea through drawing of a schematic map of oceanic currents. For this purpose, thirty two earth science teachers participated in the six-hour long training of learning and practice related to ocean current schematic map. The teacher participants performed drawing of the ocean current schematic map of the East Sea in three different phases, i.e.; pre-, post-, and delayed-post phase. In addition, all the maps conducted by participants were converted to digitalized image data. Detailed analysis were performed to investigate participating teachers' knowledge about the currents of the East Sea. Findings are as follows: First, the teacher participants have background knowledge about the ocean current map, but it reveals an incorrect knowledge about some concepts. Second, after teacher training, teachers' knowledge increased about the East Sea's currents, while a decrease was found in the differences between individual teachers' knowledge. This pattern was more evident in the delayed-post phase of drawing than in the post-phase occurred immediately after training. Third, the teacher participants were strongly aware of the need to improve the ocean current schematic map of the East Sea in science textbook in terms of scientific knowledge. In addition, they showed a high level of satisfaction about teacher training because they perceived that it was meaningful in various aspects; recognizing the importance of content knowledge and conjunction with instructional strategies, the needs of secondary science curriculum, and recognition of the nature of scientific knowledge. The results imply that teachers' subject matter knowledge plays a significant role to make science teaching effective.