• KIPS Transactions on Software and Data Engineering
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• v.12 no.4
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• pp.173-178
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• 2023

As Korean literature spreads around the world, its position in the overseas publishing market has become important. As demand in the overseas publishing market continues to grow, it is essential to predict future book sales and analyze the characteristics of books that have been highly favored by overseas readers in the past. In this study, we proposed ensemble learning based prediction model and analyzed characteristics of the cumulative sales of more than 5,000 copies classified as good sellers published overseas over the past 5 years. We applied the five ensemble learning models, i.e., XGBoost, Gradient Boosting, Adaboost, LightGBM, and Random Forest, and compared them with other machine learning algorithms, i.e., Support Vector Machine, Logistic Regression, and Deep Learning. Our experimental results showed that the ensemble algorithm outperforms other approaches in troubleshooting imbalanced data. In particular, the LightGBM model obtained an AUC value of 99.86% which is the best prediction performance. Among the features used for prediction, the most important feature is the author's number of overseas publications, and the second important feature is publication in countries with the largest publication market size. The number of evaluation participants is also an important feature. In addition, text mining was performed on the four book reviews that sold the most among good-selling books. Many reviews were interested in stories, characters, and writers and it seems that support for translation is needed as many of the keywords of "translation" appear in low-rated reviews.

• The Journal of Bigdata
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• v.7 no.1
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• pp.213-224
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• 2022

In logistics and distribution, Market Basket Analysis (MBA) is used as an important means to analyze the correlation between major sales products and to increase internal operational efficiency. In particular, the results of market basket analysis are used as important reference data for decision-making processes such as product purchase prediction, product recommendation, and product display structure in stores. With the recent development of e-commerce, the number of items handled by a single distribution and logistics company has rapidly increased, And the existing analytical methods such as Apriori and FP-Growth have slowed down due to the exponential increase in the amount of calculation and applied to actual business. There is a limit to examining important association rules to overcome this limitation, In this study, at the Main-Category level, which is the highest classification system of products, the utility item set mining technique that can consider the sales volume of products together was used to first select a group of products mainly sold together. Then, at the sub-category level, the types of products sold together were identified using FP-Growth. By using this sequential layer filtering technique, it may be possible to reduce the unnecessary calculations and to find practically usable rules for enhancing the effectiveness and profitability.

• The Journal of Bigdata
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• v.8 no.1
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• pp.131-156
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• 2023

The traditional retail industry significantly changed over the past decade due to the mobile and online technologies. This change has been accompanied by a shift in consumer behavior regarding purchasing patterns. Despite the rise of online shopping, there are still specific categories of products, such as "Processed food" in Mongolia, for which traditional shopping remains the preferred purchase method. To prepare for the inevitable future of retail businesses, firms need to closely analyze the performance of their offline stores to plan their further actions in a new multi-channel environment. Retailers must integrate diverse channels into their operations to stay relevant and adjust to the shifting market. In this research, we have analyzed the performance data such as sales, profit, and amount of sales of offline stores by using clustering approach. From the clustering, we have found the several distinct insights by comparing the circumstances and performance of retail stores. For the certain retail stores, we have proposed three different strategies: a fulfillment hub store between online and offline channels, an experience store to elongate customers' time on the premises, and a merge between two non-related channels that could complement each other to increase traffic based on the store characteristics. With the proposed strategies, it may enhance the user experience and profit at the same time.

• Journal of Service Research and Studies
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• v.14 no.1
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• pp.27-43
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• 2024

The market share of online platform services in the used car market continues to expand. And The used car online platform service provides service users with specifications of vehicles, accident history, inspection details, detailed options, and prices of used cars. SUV vehicle type's share in the domestic automobile market will be more than 50% in 2023, Sales of Hybrid vehicle type are doubled compared to last year. And these vehicle types are also gaining popularity in the used car market. Prior research has proposed a used car price prediction model by executing a Machine Learning model for all vehicles or vehicles by brand. On the other hand, the popularity of SUV and Hybrid vehicles in the domestic market continues to rise, but It was difficult to find a study that proposed a used car price prediction model for these vehicle type. This study selects a used car price prediction model by vehicle type using vehicle specifications and options for Sedans, SUV, and Hybrid vehicles produced by domestic brands. Accordingly, after selecting feature through the Lasso regression model, which is a feature selection, the ensemble model was sequentially executed with the same sampling, and the best model by vehicle type was selected. As a result, the best model for all models was selected as the CBR model, and the contribution and direction of the features were confirmed by visualizing Tree SHAP Value for the best model for each model. The implications of this study are expected to propose a used car price prediction model by vehicle type to sales officials using online platform services, confirm the attribution and direction of features, and help solve problems caused by asymmetry fo information between them.

• Information Systems Review
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• v.25 no.1
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• pp.213-231
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• 2023

With the rapid development of live streaming commerce, online host as an information source plays a critical role in affecting live streaming performance. However, the impact of different product types on the relationship between online hosts and live streaming has been less studied. Based on the elaboration likelihood model (ELM) and information source theory, this study aims to empirically investigate what factors influence the sales of live streaming commerce and how product type moderates the relationship between them. The analysis of 11,422 live streaming commerce data collected for four months from October 10, 2021 to February 10, 2022 shows that, among the factors related to source credibility and attractiveness, multi-channel networks (MCN) and the number of followers positively affect the sales volume of live streaming commerce, whereas the reputation score harms the sales. Moreover, the moderating effect of the product type (i.e., ratio of involvement products) on the relationships is confirmed. The findings enrich the literature on live streaming commerce performance. The limitations and future research directions are also discussed.

• Information Systems Review
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• v.23 no.1
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• pp.45-68
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• 2021

As the influence of online reviews on consumer decision-making increases, concerns about review manipulation are also increasing. Fake reviews or review manipulations are emerging as an important problem by posting untrue reviews in order to increase sales volume, causing the consumer's reverse choice, and acting at a high cost to the society as a whole. Most of the related prior studies have focused on predicting review manipulation through data mining methods, and research from a consumer perspective is insufficient. However, since the possibility of manipulation of reviews perceived by consumers can affect the usefulness of reviews, it can provide important implications for online word-of-mouth management regardless of whether it is false or not. Therefore, in this study, we analyzed whether there is a difference between the review evaluated by the consumer as being manipulated and the general review, and verified whether the manipulated review negatively affects the review usefulness. For empirical analysis, 34,711 online book reviews on the LibraryThing website were analyzed using multilevel logistic regression analysis and Poisson regression analysis. As a result of the analysis, it was found that there were differences in product level, reviewer level, and review level factors between reviews that consumers perceived as being manipulated and reviews that were not. In addition, manipulated reviews have been shown to negatively affect review usefulness.

• Journal of Food Hygiene and Safety
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• v.21 no.1
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• pp.41-46
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• 2006

This study was carried out to survey the prevalence of foodborne pathogens in Sangshik products and their raw materials far the purpose of ensuring safety of those products in market, and establishing microbial regulatory standard. From 2002 to 2004, a total of 191 Sangshik products were purchased from market or mail-order sales, and major foodborne pathogens; E. coli, Salmonella, Staphylococcus aureus, Bacillus cereus, Clostridium perfringens, Listeria monocytogenes, Campylobacter jejuni, Yersinia enterocolitica, E. coli O157:H7, Vibrio parahaemolyticus were tested. B. cereus, C. perfringens and E. coli were detected from 29 samples (15.2%), 21 samples (11.0%) and 1 sample (0.5%), respectively. But other tested bacteria were not detected. For the identification of contamination source, 53 Sangshik ingredients were collected from 9 different manufacture factories. The results were similar with the Sangshik products. Aerobic plate counts were ranging from $1.0X10^3cfu/g\;to\;1.5X10^8cfu/g$. B. cereus was detected from 13 samples (24.5%), and counted as less than 100 cfu/g. C. perfringens were detected from 2 samples (3.8%), and counted as less than 100 cfu/g. Other foodborne pathogens were not detected except for B. cereus and C. perfringens. From the results, it was revealed that potential of microbial hazard by Sangshik was relatively low. However, it would be suggested that hygienic management and controling be needed for the prevention of growing contaminated pathogens and cross contamination during process and sale due to improper storage and management.

• Microbiology and Biotechnology Letters
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• v.35 no.1
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• pp.17-25
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• 2007

The kinds and quantity of antimicrobial agents used for cattle (animal industry) may be considerable, suggesting the possibility that pathogenic bacteria which cannot be extirpated by the existing antimicrobial agents could appear. Ten cattle, pig and chicken farms, respectively, were randomly selected from 5 provinces in Korea and the samples were collected from excrement, manure, underground water, farmers' hands and the neishboring environment. h total of 299 samples were examined and 197 of Escherichia coli, 13 of Campylobacter jejun/coli, 223 of Enterococcus faecium/faecalis and 42 of Staphylococcus aureus isolates were collected. All isolates were screened for antimicrobial resistance: 69.4% of E. coli (137/197 strains), 78.6% of S. aureus (33/42 strains), and 82.1% of E. faecium/faecalis (183/223 strains) were resistant to one antimicrobial agent and all of C. jejuni/coli Isolates showed the resistance to one antimicrobial agent. Meanwhile, the multiple resistance ratio for more than 4 lines of antimicrobial agent was 19.2% of E. coli (38/197 strains), 11.9% of S. aureus (5/42 strains), 15.4% of C. jejuni/coli (2/13 strains) and 6.2% of E. faecium/faecalis (14/223 strains). The antimicrobial resistance ratio of bacteria isolated from the cattle farm showed lower than that of bacteria isolated from the pig or chicken farm, which might be related to the quantify of antimicrobial agents consumed. And one strain of vancomycin resistant E..faecium (VREF) were isolated from the excrement of chicken and stream, respectively. Generally, the ratio of VREF collected in animal farm environments is lower than that of VREF collected in medical environment.

• Journal of Nutrition and Health
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• v.42 no.5
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• pp.486-495
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• 2009

The purpose of this study was to analyze the performance evaluation index for a salt reduction project. Questionnaires were developed in order to investigate salt reduction programs nationwide. The evaluation index and programs were analyzed through the case study of a salt reduction program in public health centers. The validity of the salt reduction program's evaluation index was determined based on study of the Delphi survey and on discussion with nutrition and health care professionals. The Delphi survey showed that daily salt intake was the most valid nutritional evaluation index. Stroke mortality and stomach cancer mortality were good health care evaluation indexes. The method for measuring salt intake that had the greatest validity was a 24-hour urine collection. However, 24-hour urine collection had the lowest score for ease of performance. The combined scores of validity and ease of performance showed that the survey method for dietary attitude and dietary behavior, dietary frequency analysis (DFQ 15), and a salty taste assessment, in that order, were proper methods. The high reliability of the salty taste assessment indicated that the percentage of the population that exhibits proper salt intake (2,000 mg sodium or less daily) and the percentage of the population that consumes low-salt diets as nutritional evaluation indexes also will be helpful to evaluate performance of salt reduction programs.

• Journal of Intelligence and Information Systems
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• v.18 no.4
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• pp.1-17
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• 2012

Personalized smart devices such as smartphones and smart pads are widely used. Unlike traditional feature phones, theses smart devices allow users to choose a variety of functions, which support not only daily experiences but also business operations. Actually, there exist a huge number of applications accessible by smart device users in online and mobile application markets. Users can choose apps that fit their own tastes and needs, which is impossible for conventional phone users. With the increase in app demand, the tastes and needs of app users are becoming more diverse. To meet these requirements, numerous apps with diverse functions are being released on the market, which leads to fierce competition. Unlike offline markets, online markets have a limitation in that purchasing decisions should be made without experiencing the items. Therefore, online customers rely more on item-related information that can be seen on the item page in which online markets commonly provide details about each item. Customers can feel confident about the quality of an item through the online information and decide whether to purchase it. The same is true of online app markets. To win the sales competition against other apps that perform similar functions, app developers need to focus on writing app descriptions to attract the attention of customers. If we can measure the effect of app descriptions on sales without regard to the app's price and quality, app descriptions that facilitate the sale of apps can be identified. This study intends to provide such a quantitative result for app developers who want to promote the sales of their apps. For this purpose, we collected app details including the descriptions written in Korean from one of the largest app markets in Korea, and then extracted keywords from the descriptions. Next, the impact of the keywords on sales performance was measured through our econometric model. Through this analysis, we were able to analyze the impact of each keyword itself, apart from that of the design or quality. The keywords, comprised of the attribute and evaluation of each app, are extracted by a morpheme analyzer. Our model with the keywords as its input variables was established to analyze their impact on sales performance. A regression analysis was conducted for each category in which apps are included. This analysis was required because we found the keywords, which are emphasized in app descriptions, different category-by-category. The analysis conducted not only for free apps but also for paid apps showed which keywords have more impact on sales performance for each type of app. In the analysis of paid apps in the education category, keywords such as 'search+easy' and 'words+abundant' showed higher effectiveness. In the same category, free apps whose keywords emphasize the quality of apps showed higher sales performance. One interesting fact is that keywords describing not only the app but also the need for the app have asignificant impact. Language learning apps, regardless of whether they are sold free or paid, showed higher sales performance by including the keywords 'foreign language study+important'. This result shows that motivation for the purchase affected sales. While item reviews are widely researched in online markets, item descriptions are not very actively studied. In the case of the mobile app markets, newly introduced apps may not have many item reviews because of the low quantity sold. In such cases, item descriptions can be regarded more important when customers make a decision about purchasing items. This study is the first trial to quantitatively analyze the relationship between an item description and its impact on sales performance. The results show that our research framework successfully provides a list of the most effective sales key terms with the estimates of their effectiveness. Although this study is performed for a specified type of item (i.e., mobile apps), our model can be applied to almost all of the items traded in online markets.