• Journal of the Korean Institute of Traditional Landscape Architecture
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• v.29 no.4
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• pp.124-136
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• 2011

In landscape architecture, plants play an important role in realizing the intention of the architect and user- behavior as well as an ecology and appearance of the space for them. However, it is true that many researches have focused on ecological characteristics of plants, their cultivation environment and symbolic meanings in traditional terms, while relatively few for the analysis of the aspects of each period through plants. For this, cherry trees that we often see around are selected and their introduction, propagation, development and symbolism from the view of chronicle are studied and the results are followings; Firstly, three-year seedlings of 1,500 pieces of cherry tree from Osaka and Tokyo were planted for the first time in Oieseongdae, Namsan Park, Seoul. Since then, they had been widely planted at traditional sites, modern parks, newly-constructed roads for street trees, and for this, the Japanese Government-General of Chosun had actively supported by its direct cultivation and selling of cherry trees. The spread of cherry trees planted raised the question of whether or not Prunus yedoensis is originated from Jeju Island. Secondly, such massive and artificial planting of them had become attractions over the time and mass media at that time also had actively promoted it. And such trend made the day and night picnic under the cherry blossoms one of the most representative cultures of enjoying spring in Seoul. Thirdly, although general people enjoyed cherry blossoms, but they had dual view and attitude for cherry trees, which were well expressed in their use of them: for example, cherry blossoms, aeng and sakura were used altogether for same meaning, but night aeng or night picnic under cherry blossoms were especially used instead of yojakura when mentioning just pleasure, which meant some saw night enjoying cherry blossoms a low culture. Fourth, symbolic space of Chosun had been transformed into the space for enjoyment and consumption. Anyone who paid entrance fee could enjoy performance of revugirl, cinema and entertainment along with enjoying cherry blossoms. The still-existing strict differentiation of enjoyment culture by social status, class and ethnicity was dismantled from that trend and brought about a kind of disorder. From this, we could find that cherry blossoms had made a great contribution to the change of traditional enjoyment culture over the Japanese colonial period and become a popular spring enjoyment.

• 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.