• Journal of Plant Biotechnology
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• v.48 no.3
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• pp.131-138
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• 2021

We reviewed current research trends for discriminating between species of the Angelica genus, a group of important medicinal plants registered in South Korea, China, and Japan. Since the registered species for medicinal purposes differ by country, they are often adulterated as well as mixed in commercial markets. Several DNA technologies have been applied to distinguish between species. However, one of the restrictions is insufficient single-nucleotide polymorphisms (SNPs) within the target DNA fragments; in particular, among closely-related species. Recently, amplification refractory mutation system (ARMS)-PCR and highresolution melting (HRM) curve analysis techniques have been developed to solve such a problem. We applied both technologies, and found they were able to discriminate several lines of Angelica genus, including A. gigas Nakai, A. gigas Jiri, A. sinensis, A. acutiloba Kitag, and Levisticum officinale. Furthermore, although the ITS region differs only by one SNP between A. gigas Nakai and A. gigas Jiri, both HRM and ARMS-PCR techniques were powerful enough to discriminate between them. Since both A. gigas Nakai and A. gigas Jiri are native species to South Korea and are very closely related, they are difficult to discriminate by their morphological characteristics. For practical applications of these technologies, further research is necessary with various materials, such as dried or processed materials (jam, jelly, juice, medicinal decoctions, etc.) in commercial markets.

• Journal of the Korea Society of Computer and Information
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• v.26 no.1
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• pp.265-274
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• 2021

In this paper, we propose a novel recommendation model based on Doc2vec using search keywords and product details. Until now, a lot of prior studies on recommender systems have proposed collaborative filtering (CF) as the main algorithm for recommendation, which uses only structured input data such as customers' purchase history or ratings. However, the use of unstructured data like online customer review in CF may lead to better recommendation. Under this background, we propose to use search keyword data and product detail information, which are seldom used in previous studies, for product recommendation. The proposed model makes recommendation by using CF which simultaneously considers ratings, search keywords and detailed information of the products purchased by customers. To extract quantitative patterns from these unstructured data, Doc2vec is applied. As a result of the experiment, the proposed model was found to outperform the conventional recommendation model. In addition, it was confirmed that search keywords and product details had a significant effect on recommendation. This study has academic significance in that it tries to apply the customers' online behavior information to the recommendation system and that it mitigates the cold start problem, which is one of the critical limitations of CF.

• Journal of Intelligence and Information Systems
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• v.18 no.4
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• pp.19-42
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• 2012

Consumer consumption patterns are shifting rapidly as buyers migrate from offline markets to e-commerce routes, such as shopping channels on TV and internet shopping malls. In the offline markets consumers go shopping, see the shopping items, and choose from them. Recently consumers tend towards buying at shopping sites free from time and place. However, as e-commerce markets continue to expand, customers are complaining that it is becoming a bigger hassle to shop online. In the online shopping, shoppers have very limited information on the products. The delivered products can be different from what they have wanted. This case results to purchase cancellation. Because these things happen frequently, they are likely to refer to the consumer reviews and companies should be concerned about consumer's voice. E-commerce is a very important marketing tool for suppliers. It can recommend products to customers and connect them directly with suppliers with just a click of a button. The recommender system is being studied in various ways. Some of the more prominent ones include recommendation based on best-seller and demographics, contents filtering, and collaborative filtering. However, these systems all share two weaknesses : they cannot recommend products to consumers on a personal level, and they cannot recommend products to new consumers with no buying history. To fix these problems, we can use the information which has been collected from the questionnaires about their demographics and preference ratings. But, consumers feel these questionnaires are a burden and are unlikely to provide correct information. This study investigates combining collaborative filtering with the centrality of social network analysis. This centrality measure provides the information to infer the preference of new consumers from the shopping history of existing and previous ones. While the past researches had focused on the existing consumers with similar shopping patterns, this study tried to improve the accuracy of recommendation with all shopping information, which included not only similar shopping patterns but also dissimilar ones. Data used in this study, Movie Lens' data, was made by Group Lens research Project Team at University of Minnesota to recommend movies with a collaborative filtering technique. This data was built from the questionnaires of 943 respondents which gave the information on the preference ratings on 1,684 movies. Total data of 100,000 was organized by time, with initial data of 50,000 being existing customers and the latter 50,000 being new customers. The proposed recommender system consists of three systems : [+] group recommender system, [-] group recommender system, and integrated recommender system. [+] group recommender system looks at customers with similar buying patterns as 'neighbors', whereas [-] group recommender system looks at customers with opposite buying patterns as 'contraries'. Integrated recommender system uses both of the aforementioned recommender systems to recommend movies that both recommender systems pick. The study of three systems allows us to find the most suitable recommender system that will optimize accuracy and customer satisfaction. Our analysis showed that integrated recommender system is the best solution among the three systems studied, followed by [-] group recommended system and [+] group recommender system. This result conforms to the intuition that the accuracy of recommendation can be improved using all the relevant information. We provided contour maps and graphs to easily compare the accuracy of each recommender system. Although we saw improvement on accuracy with the integrated recommender system, we must remember that this research is based on static data with no live customers. In other words, consumers did not see the movies actually recommended from the system. Also, this recommendation system may not work well with products other than movies. Thus, it is important to note that recommendation systems need particular calibration for specific product/customer types.

• Pediatric Infection and Vaccine
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• v.23 no.3
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• pp.172-179
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• 2016

Purpose: Bacillus cereus has been reported as the cause of nosocomial infections in cancer patients. In our pediatric cancer ward, a sudden rise in the number of patients with B. cereus bacteremia was observed in 2013 to 2014. This study was performed to investigate the molecular epidemiology of increased B. cereus bacteremia cases in our center. Methods: Pediatric cancer patients who developed B. cereus bacteremia were identified from January 2001 to June 2014. The B. cereus bacteremia in this study was defined as a case in which at least one B. cereus identified in blood cultures, regardless of true bacteremia. Available isolates were further tested by multilocus sequence typing (MLST) analysis. A retrospective chart review was performed. Results: Nineteen patients developed B. cereus bacteremia during the study period. However, in 2013, a sudden increase in the number of patients with B. cereus bacteremia was observed. In addition, three patients developed B. cereus bacteremia within 1 week in July and the other three patients within 1 week in October, respectively, during emergency room renovation. However, MLST analysis revealed different sequence types without consistent patterns. Before 2013, five tested isolates were ST18, ST26, ST177, and ST147-like type, and ST219-like type. Isolates from 2013 were ST18, ST73, ST90, ST427, ST784, ST34-like type, and ST130-like type. Conclusions: MLST analyses showed variable ST distribution of B. cereus isolates. Based on this study, there was no significant evidence suggesting a true outbreak caused by a single ST among patients who developed B. cereus bacteremia.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.43-56
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• 2020

Recommender systems help users make the best choice among various options. Especially, recommender systems play important roles in internet sites as digital information is generated innumerable every second. Many studies on recommender systems have focused on an accurate recommendation. However, there are some problems to overcome in order for the recommendation system to be commercially successful. First, there is a lack of transparency in the recommender system. That is, users cannot know why products are recommended. Second, the recommender system cannot immediately reflect changes in user preferences. That is, although the preference of the user's product changes over time, the recommender system must rebuild the model to reflect the user's preference. Therefore, in this study, we proposed a recommendation methodology using topic modeling and sequential association rule mining to solve these problems from review data. Product reviews provide useful information for recommendations because product reviews include not only rating of the product but also various contents such as user experiences and emotional state. So, reviews imply user preference for the product. So, topic modeling is useful for explaining why items are recommended to users. In addition, sequential association rule mining is useful for identifying changes in user preferences. The proposed methodology is largely divided into two phases. The first phase is to create user profile based on topic modeling. After extracting topics from user reviews on products, user profile on topics is created. The second phase is to recommend products using sequential rules that appear in buying behaviors of users as time passes. The buying behaviors are derived from a change in the topic of each user. A collaborative filtering-based recommendation system was developed as a benchmark system, and we compared the performance of the proposed methodology with that of the collaborative filtering-based recommendation system using Amazon's review dataset. As evaluation metrics, accuracy, recall, precision, and F1 were used. For topic modeling, collapsed Gibbs sampling was conducted. And we extracted 15 topics. Looking at the main topics, topic 1, top 3, topic 4, topic 7, topic 9, topic 13, topic 14 are related to "comedy shows", "high-teen drama series", "crime investigation drama", "horror theme", "British drama", "medical drama", "science fiction drama", respectively. As a result of comparative analysis, the proposed methodology outperformed the collaborative filtering-based recommendation system. From the results, we found that the time just prior to the recommendation was very important for inferring changes in user preference. Therefore, the proposed methodology not only can secure the transparency of the recommender system but also can reflect the user's preferences that change over time. However, the proposed methodology has some limitations. The proposed methodology cannot recommend product elaborately if the number of products included in the topic is large. In addition, the number of sequential patterns is small because the number of topics is too small. Therefore, future research needs to consider these limitations.