• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.23-46
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• 2021

Collaborative filtering, which is often used in personalization recommendations, is recognized as a very useful technique to find similar customers and recommend products to them based on their purchase history. However, the traditional collaborative filtering technique has raised the question of having difficulty calculating the similarity for new customers or products due to the method of calculating similaritiesbased on direct connections and common features among customers. For this reason, a hybrid technique was designed to use content-based filtering techniques together. On the one hand, efforts have been made to solve these problems by applying the structural characteristics of social networks. This applies a method of indirectly calculating similarities through their similar customers placed between them. This means creating a customer's network based on purchasing data and calculating the similarity between the two based on the features of the network that indirectly connects the two customers within this network. Such similarity can be used as a measure to predict whether the target customer accepts recommendations. The centrality metrics of networks can be utilized for the calculation of these similarities. Different centrality metrics have important implications in that they may have different effects on recommended performance. In this study, furthermore, the effect of these centrality metrics on the performance of recommendation may vary depending on recommender algorithms. In addition, recommendation techniques using network analysis can be expected to contribute to increasing recommendation performance even if they apply not only to new customers or products but also to entire customers or products. By considering a customer's purchase of an item as a link generated between the customer and the item on the network, the prediction of user acceptance of recommendation is solved as a prediction of whether a new link will be created between them. As the classification models fit the purpose of solving the binary problem of whether the link is engaged or not, decision tree, k-nearest neighbors (KNN), logistic regression, artificial neural network, and support vector machine (SVM) are selected in the research. The data for performance evaluation used order data collected from an online shopping mall over four years and two months. Among them, the previous three years and eight months constitute social networks composed of and the experiment was conducted by organizing the data collected into the social network. The next four months' records were used to train and evaluate recommender models. Experiments with the centrality metrics applied to each model show that the recommendation acceptance rates of the centrality metrics are different for each algorithm at a meaningful level. In this work, we analyzed only four commonly used centrality metrics: degree centrality, betweenness centrality, closeness centrality, and eigenvector centrality. Eigenvector centrality records the lowest performance in all models except support vector machines. Closeness centrality and betweenness centrality show similar performance across all models. Degree centrality ranking moderate across overall models while betweenness centrality always ranking higher than degree centrality. Finally, closeness centrality is characterized by distinct differences in performance according to the model. It ranks first in logistic regression, artificial neural network, and decision tree withnumerically high performance. However, it only records very low rankings in support vector machine and K-neighborhood with low-performance levels. As the experiment results reveal, in a classification model, network centrality metrics over a subnetwork that connects the two nodes can effectively predict the connectivity between two nodes in a social network. Furthermore, each metric has a different performance depending on the classification model type. This result implies that choosing appropriate metrics for each algorithm can lead to achieving higher recommendation performance. In general, betweenness centrality can guarantee a high level of performance in any model. It would be possible to consider the introduction of proximity centrality to obtain higher performance for certain models.

• Journal of Nutrition and Health
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• v.55 no.4
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• pp.476-491
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• 2022

Purpose: The purpose of this study was to evaluate the energy and nutrient contents of home meal replacement (HMR) rice products per single serving package based on nutrition labels. Methods: The market research was conducted from February to July 2021 on products sold on the internet, at convenience stores, etc. A total of 406 products were investigated. The products were divided into the following 6 classifications: instant rice (n = 45), cup rice (n = 64), frozen rice (n = 188), rice bowls with toppings (n = 32), gimbap (n = 38), and triangular gimbap (n = 39). Results: The mean packaging weight per serving was the highest in the rice bowl with toppings at 297.1 g, followed by cup rice (264.0 g), frozen rice (239.5 g), gimbap (230.2 g), instant rice (193.4 g), and triangular gimbap (121.6 g) (p < 0.001). The energy per serving package for the rice bowl with toppings was significantly the highest at 496.0 kcal (p < 0.001). The sodium content per serving package of gimbap was the highest at 1,021.8 mg and that of the instant rice was lowest at 37.4 mg (p < 0.001). The price per serving package of the rice bowl with toppings at 4,333.8 won was the highest. The contribution to the daily nutritional value per serving package of all types of HMR rice products surveyed showed an average range of 10-25% for energy, 11-22% for carbohydrates, and 2-51% for sodium. Conclusion: These results indicate the energy and nutrient contents of HMR rice products, vary by type. Therefore, consumers should review the nutrition labeling to select an appropriate HMR rice product based on their intended consumption.

• Journal of Wetlands Research
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• v.25 no.1
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• pp.35-47
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• 2023

Korean facility horticulture and hydroponic cultivation methods increase, requiring the management of waste water generated. In this study, the amount of fertilizer contained in the discharged waste liquid was determined. By evaluating this as a price, it was suggested to reduce water treatment costs and recycle fertilizer components. It was evaluated based on the results of major water quality analysis of waste liquid by crop, such as tomatoes, paprika, cucumbers, and strawberries, and in the case of P component, it was analyzed by converting it to the amount of phosphoric acid (P₂O₅). The amount of nitrogen (N) can be calculated by discharging 1,145.90kg·ha^-1 of tomatoes, 920.43kg·ha^-1 of paprika, 804.16kg·ha^-1 of cucumbers, 405.83kg·ha^-1 of strawberries, and the fertilizer content of P₂O₅ is 830.65kg·ha^-1 of paprika, 622.32kg·ha^-1 of tomatoes, 477.67kg·ha^-1 of cucumbers. In addition, trace elements such as potassium (K), calcium (Ca), magnesium (Mg), iron (Fe), and manganese (Mn) were also analyzed to be emitted. The price per kg of each item calculated by averaging the price of fertilizer sold on the market can be evaluated as KRW, N 860.7, P 2,378.2, K 2,121.7, Ca 981.2, Mg 1,036.3, Fe 126,076.9, Mn 62,322.1, Zn 15,825.0, Cu 31,362.0, B 4,238.0, Mo 149,041.7. The annual fertilizer loss amount for each crop was calculated by comprehensively considering the price per kg calculated based on the market price of fertilizer, the concentration of waste by crop analyzed earlier, and the average annual emission of hydroponic cultivation. As a result of the analysis, the average of the four hydroponic crops was 5,475,361.1 won in fertilizer ingredients, with tomatoes valued at 6,995,622.3 won, paprika valued at 7,384,923.8 won, cucumbers valued at 5,091,607.9 won, and strawberries valued at 2,429,290.6 won. It was expected that if hydroponic drainage is managed through self-treatment or threshing before discharge rather than by leaking it into a river and treating it as a pollutant, it can be a valuable reusable fertilizer ingredient along with reducing water treatment costs.

• Journal of the Korean Institute of Traditional Landscape Architecture
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• v.41 no.2
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• pp.35-45
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• 2023

First, The spatial distribution characteristics of old big trees were analyzed using ArcGIS figures by combining basic information such as species and ages of old big trees in Handan City, which were compiled by the local bureau of landscaping. The types of species, distribution by ages of trees, ownership status, growth status, and diversity status were comprehensively analyzed. Statistically, Styphnolobium, Acacia, Gleditsia, and Albizia of Fabaceae accounted for the majority, of which Sophora japonica accounted for the highest proportion. Sophora japonica is widely and intensively distributed to each prefecture and district in Handan city. According to the age and distribution, the old big trees over 1000 years old were mainly Sophora japonica, Zelkova serrata, Juniperus chinensis, Morus australis Koidz., Dalbergia hupeana Hance, Ceratonia siliqua L., and Pistacia chinensis, and Platycladus orientalis. Second, as found in each type of old big tree status, various types of old big tree status were investigated, the protection management system, protection management process, and protection management benefits were studied, and the protection of old big tree was closely related to the growth environment. Currently, the main driving force behind the protection of old big trees is the worship of old big trees. By depositing its sacredness to the old big tree and sublimating the natural character that nature gave to the old big tree into a guiding consciousness of social activities, nature's "beauty" and personality's "goodness" are well combined. The protection state of the old big tree is closely related to the degree of interaction with the surrounding environment and the participation of various cultures and subjects. In the process of continuously interacting with the surrounding environment during the long-term growth of old big trees, it seems that a natural sanctuary was formed around old big trees in the process of voluntarily establishing a "natural-cultural-scape" system involving bottom-up and top-down cross-regions, multicultural and multi-subjects. Third, China focused on protecting and recovering old big trees, but the protection management system is poor due to a lack of comprehensive consideration of historical and cultural values, plant diversity significance, and social values of old big trees in the management process. Three indicators of space's regional characteristics, property and protection characteristics, and value characteristics can be found in the evaluation of the natural characteristics of old giant trees, which are highly valuable in terms of traditional consciousness management, resource protection practice, faith system construction, and realization of life community values. A systematic management system should be supported as to whether they can be protected and developed for a long time. Fourth, as the perception of protected areas is not yet mature in China, "natural sanctuary" should be treated as an important research content in the process of establishing a nature reserve system. The form of natural sanctuary management, which focuses on bottom-up community participation, is a strong supplement to the current type of top-down nature reserve management in China. Based on this, the protection of old giant trees should be included in the form of a nature reserve called a natural monument in the nature reserve system. In addition, residents of the area around the nature reserve should be one of the main agents of biodiversity conservation.

• Journal of Intelligence and Information Systems
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• v.24 no.1
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• pp.227-252
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• 2018

The evolution of instant communication has mirrored the development of the Internet and messenger applications are among the most representative manifestations of instant communication technologies. In messenger applications, senders use emoticons to supplement the emotions conveyed in the text of their messages. The fact that communication via messenger applications is not face-to-face makes it difficult for senders to communicate their emotions to message recipients. Emoticons have long been used as symbols that indicate the moods of speakers. However, at present, emoticon-use is evolving into a means of conveying the psychological states of consumers who want to express individual characteristics and personality quirks while communicating their emotions to others. The fact that companies like KakaoTalk, Line, Apple, etc. have begun conducting emoticon business and sales of related content are expected to gradually increase testifies to the significance of this phenomenon. Nevertheless, despite the development of emoticons themselves and the growth of the emoticon market, no suitable emoticon recommendation system has yet been developed. Even KakaoTalk, a messenger application that commands more than 90% of domestic market share in South Korea, just grouped in to popularity, most recent, or brief category. This means consumers face the inconvenience of constantly scrolling around to locate the emoticons they want. The creation of an emoticon recommendation system would improve consumer convenience and satisfaction and increase the sales revenue of companies the sell emoticons. To recommend appropriate emoticons, it is necessary to quantify the emotions that the consumer sees and emotions. Such quantification will enable us to analyze the characteristics and emotions felt by consumers who used similar emoticons, which, in turn, will facilitate our emoticon recommendations for consumers. One way to quantify emoticons use is metadata-ization. Metadata-ization is a means of structuring or organizing unstructured and semi-structured data to extract meaning. By structuring unstructured emoticon data through metadata-ization, we can easily classify emoticons based on the emotions consumers want to express. To determine emoticons' precise emotions, we had to consider sub-detail expressions-not only the seven common emotional adjectives but also the metaphorical expressions that appear only in South Korean proved by previous studies related to emotion focusing on the emoticon's characteristics. We therefore collected the sub-detail expressions of emotion based on the "Shape", "Color" and "Adumbration". Moreover, to design a highly accurate recommendation system, we considered both emotion-technical indexes and emoticon-emotional indexes. We then identified 14 features of emoticon-technical indexes and selected 36 emotional adjectives. The 36 emotional adjectives consisted of contrasting adjectives, which we reduced to 18, and we measured the 18 emotional adjectives using 40 emoticon sets randomly selected from the top-ranked emoticons in the KakaoTalk shop. We surveyed 277 consumers in their mid-twenties who had experience purchasing emoticons; we recruited them online and asked them to evaluate five different emoticon sets. After data acquisition, we conducted a factor analysis of emoticon-emotional factors. We extracted four factors that we named "Comic", Softness", "Modernity" and "Transparency". We analyzed both the relationship between indexes and consumer attitude and the relationship between emoticon-technical indexes and emoticon-emotional factors. Through this process, we confirmed that the emoticon-technical indexes did not directly affect consumer attitudes but had a mediating effect on consumer attitudes through emoticon-emotional factors. The results of the analysis revealed the mechanism consumers use to evaluate emoticons; the results also showed that consumers' emoticon-technical indexes affected emoticon-emotional factors and that the emoticon-emotional factors affected consumer satisfaction. We therefore designed the emoticon recommendation system using only four emoticon-emotional factors; we created a recommendation method to calculate the Euclidean distance from each factors' emotion. In an attempt to increase the accuracy of the emoticon recommendation system, we compared the emotional patterns of selected emoticons with the recommended emoticons. The emotional patterns corresponded in principle. We verified the emoticon recommendation system by testing prediction accuracy; the predictions were 81.02% accurate in the first result, 76.64% accurate in the second, and 81.63% accurate in the third. This study developed a methodology that can be used in various fields academically and practically. We expect that the novel emoticon recommendation system we designed will increase emoticon sales for companies who conduct business in this domain and make consumer experiences more convenient. In addition, this study served as an important first step in the development of an intelligent emoticon recommendation system. The emotional factors proposed in this study could be collected in an emotional library that could serve as an emotion index for evaluation when new emoticons are released. Moreover, by combining the accumulated emotional library with company sales data, sales information, and consumer data, companies could develop hybrid recommendation systems that would bolster convenience for consumers and serve as intellectual assets that companies could strategically deploy.