• Journal of the Korean Institute of Traditional Landscape Architecture
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• v.39 no.4
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• pp.50-65
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• 2021

In this study, the water flow system by measuring the flow-way type and distance of flow path that composes the Gugok through literature survey, field survey, and map work on Gugok gardens in Korea whose existence has been confirmed, while investigating and analyzing watersheds, river orders, and river grades. It was intended to reveal the watershed distribution and stream morphological characteristics of the Gugok gardens and to use them as basic data for future enjoyment and conservation of the Gugok gardens. The conclusion of the study is as follows. First, Of the 93 Gugok gardens that have been confirmed to exist, it was found that 11 places(11.8%) were found to have a descending(top-down) type of Gugok that develops while descending along a stream. Second, As a result of analysis of the length of the flow path for each valley, Okryudonggugok(玉流洞九曲, Namsan-gugok) in Gimcheon, Gyeongsangbuk-do was found to have the shortest length of 0.44km among the surveyed valleys, while the flow distance of Muheulgugok(武屹九曲) located in Seongju-gun and Gimcheon-si, Gyeongsangbuk-do was 31.1km, showing the longest flowing distance. The average flow path length of the Gugok Garden in Korea was 6.24km, and the standard deviation was 4.63km, indicating that the deviation between the 'curved type'e and the 'valley type' was severe. In addition, 14(15.1%) Gugok gardens were found to be partially submerged due to dam construction. Third, As a result of analyzing the waters area where Gugok garden is located, the number of Nakdong river basins was much higher at 52 sites(55.9%), followed by the Hangang river basin at 27 sites(28.7%), the Geum river basin at 9 sites(9.7%), and the Yeongsan river and Seomjin river basins at 5(5.4%). Fourth, All Gugok gardens located in the Han river region were classified as the Han river system, and the Gugok garden located on the Nakdong river was classified as the main Nakdong river system, except for 7 places including 5 places in the Nakdong Gangnam Sea water system and 2 places in the Nakdong Gangdong sea water system. As a result of synthesizing the river order of the flow path where Gugok garden is located, Gugok, which uses the main stream as the base of Gugok, is 3 places in the Hangang water system, 5 places in the Nakdong river system, 2 places in the Geumgang water system, and 1 place in the Yeongsangam/Seomjin river system. A total of 11 locations(11.5%) were found, including 36 locations(38.2%) in the first branch, 29 locations(31.2%) in the second branch, and 16 locations(17.0%) in the third branch. And Gugok garden, located on the 4th tributary, was found to be Taehwa Five-gok(太華五曲) set in Yonghwacheon Stream in Cheorwon in the Han river system, and Hoenggyegok(橫溪九曲) in Yeongcheon Hoenggye Stream in the Nakdong river system. Fifth, As a result of the river grade analysis of the rivers located in the Gugok garden Forest, the grades of the rivers located in the Gugok garden were 13 national rivers(14.0%), 7 local first-class rivers(7.5%), and 74 local second-class rivers(78.5%) was shown.

• 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.