• Journal of Intelligence and Information Systems
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• v.23 no.4
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• pp.77-110
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• 2017

Recently, the diversification and individualization of consumption patterns through the web and mobile devices based on the Internet have been rapid. As this happens, the efficient operation of the offline store, which is a traditional distribution channel, has become more important. In order to raise both the sales and profits of stores, stores need to supply and sell the most attractive products to consumers in a timely manner. However, there is a lack of research on which SKUs, out of many products, can increase sales probability and reduce inventory costs. In particular, if a company sells products through multiple in-store stores across multiple locations, it would be helpful to increase sales and profitability of stores if SKUs appealing to customers are recommended. In this study, the recommender system (recommender system such as collaborative filtering and hybrid filtering), which has been used for personalization recommendation, is suggested by SKU recommendation method of a store unit of a distribution company that handles a homogeneous brand through a plurality of sales stores by country and region. We calculated the similarity of each store by using the purchase data of each store's handling items, filtering the collaboration according to the sales history of each store by each SKU, and finally recommending the individual SKU to the store. In addition, the store is classified into four clusters through PCA (Principal Component Analysis) and cluster analysis (Clustering) using the store profile data. The recommendation system is implemented by the hybrid filtering method that applies the collaborative filtering in each cluster and measured the performance of both methods based on actual sales data. Most of the existing recommendation systems have been studied by recommending items such as movies and music to the users. In practice, industrial applications have also become popular. In the meantime, there has been little research on recommending SKUs for each store by applying these recommendation systems, which have been mainly dealt with in the field of personalization services, to the store units of distributors handling similar brands. If the recommendation method of the existing recommendation methodology was 'the individual field', this study expanded the scope of the store beyond the individual domain through a plurality of sales stores by country and region and dealt with the store unit of the distribution company handling the same brand SKU while suggesting a recommendation method. In addition, if the existing recommendation system is limited to online, it is recommended to apply the data mining technique to develop an algorithm suitable for expanding to the store area rather than expanding the utilization range offline and analyzing based on the existing individual. The significance of the results of this study is that the personalization recommendation algorithm is applied to a plurality of sales outlets handling the same brand. A meaningful result is derived and a concrete methodology that can be constructed and used as a system for actual companies is proposed. It is also meaningful that this is the first attempt to expand the research area of the academic field related to the existing recommendation system, which was focused on the personalization domain, to a sales store of a company handling the same brand. From 05 to 03 in 2014, the number of stores' sales volume of the top 100 SKUs are limited to 52 SKUs by collaborative filtering and the hybrid filtering method SKU recommended. We compared the performance of the two recommendation methods by totaling the sales results. The reason for comparing the two recommendation methods is that the recommendation method of this study is defined as the reference model in which offline collaborative filtering is applied to demonstrate higher performance than the existing recommendation method. The results of this model are compared with the Hybrid filtering method, which is a model that reflects the characteristics of the offline store view. The proposed method showed a higher performance than the existing recommendation method. The proposed method was proved by using actual sales data of large Korean apparel companies. In this study, we propose a method to extend the recommendation system of the individual level to the group level and to efficiently approach it. In addition to the theoretical framework, which is of great value.

• Journal of the Korean Association of Geographic Information Studies
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• v.26 no.3
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• pp.52-64
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• 2023

As cities continue to develop rapidly, overcrowding, pollution, and urban sanitation problems arise, and the need to separate conflicting uses is emerging. From this perspective, there is no disagreement that urban land use should be planned. Therefore, all activities in land space must be predicted in advance and planned so that land use can be rationally established. This study used the constructed data to compare and analyze the use distribution characteristics of residential, commercial, and industrial areas in Busan Metropolitan City to identify the building area status, total floor area, and floor area ratio by use zone in districts and counties in Busan Metropolitan City. As a result, it was found that the residential area accounted for the largest proportion of the area by use zone at 51%, and that the residential area accounted for the largest proportion at 63% of the total floor area by use zone. And the analysis was conducted using a specialization coefficient that can identify regional characteristics based on land use composition ratio. Because it is difficult to determine the trend of the entire region just by counting the absolute value of the area, the area composition ratio was calculated and compared. Looking at the residential facilities among the specialization coefficients by use area, it is above 1.0 except for Gijang-gun, Sasang-gu, Saha-gu, and Jung-gu. Commercial facilities are over 1.0 except for Gijang-gun, Gangseo-gu, Nam-gu, Sasang-gu, and Saha-gu. Looking at industrial facilities, you can see that the industrial complex distribution area is Gangseo-gu (2.5), Gijang-gun (1.22), Sasang-gu (2.06), and Saha-gu (1.64). In addition, it was found that business facilities and educational welfare facilities were evenly distributed. Land use analysis was conducted through simulation of the current status of building heights according to each elevation in each use area and the height of buildings in each use area. In general, areas over 80m account for more than 43% of Busan City, showing that the distribution of use areas is designated in areas with high altitude due to the influence of topographical conditions.

• Journal of the Korean Institute of Landscape Architecture
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• v.48 no.5
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• pp.80-88
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• 2020

With the issuance of one-week fine dust emergency reduction measures in March 2019, the public's anxiety about fine dust is increasingly growing. In order to assess the application of air purifying plant-based bio-filters to public facilities, this study presented a method for measuring pollutant reduction effects by creating an indoor environment for continuous discharge of particle pollutants and conducted basic studies to verify whether indoor air quality has improved through the system. In this study conducted in a lecture room in spring, the background concentration was created by using mosquito repellent incense as a pollutant one hour before monitoring. Then, according to the schedule, the fine dust reduction capacity was monitored by irrigating for two hours and venting air for one hour. PM₁₀, PM_2.5, and temperature & humidity sensors were installed two meters front of the bio-filters, and velocity probes were installed at the center of the three air vents to conduct time-series monitoring. The average face velocity of three air vents set up in the bio-filter was 0.38±0.16 m/s. Total air-conditioning air volume was calculated at 776.89±320.16㎥/h by applying an air vent area of 0.29m×0.65m after deducing damper area. With the system in operation, average temperature and average relative humidity were maintained at 21.5-22.3℃, and 63.79-73.6%, respectively, which indicates that it satisfies temperature and humidity range of various conditions of preceding studies. When the effects of raising relatively humidity rapidly by operating system's air-conditioning function are used efficiently, it would be possible to reduce indoor fine dust and maintain appropriate relative humidity seasonally. Concentration of fine dust increased the same in all cycles before operating the bio-filter system. After operating the system, in cycle 1 blast section (C-1, β=-3.83, β=-2.45), particulate matters (PM₁₀) were lowered by up to 28.8% or 560.3㎍/㎥ and fine particulate matters (PM_2.5) were reduced by up to 28.0% or 350.0㎍/㎥. Then, the concentration of find dust (PM₁₀, PM_2.5) was reduced by up to 32.6% or 647.0㎍/㎥ and 32.4% or 401.3㎍/㎥ respectively through reduction in cycle 2 blast section (C-2, β=-5.50, β=-3.30) and up to 30.8% or 732.7㎍/㎥ and 31.0% or 459.3㎍/㎥ respectively through reduction in cycle 3 blast section (C-3, β=5.48, β=-3.51). By referring to standards and regulations related to the installation of vegetation bio-filters in public facilities, this study provided plans on how to set up objective performance evaluation environment. By doing so, it was possible to create monitoring infrastructure more objective than a regular lecture room environment and secure relatively reliable data.

• Journal of the Korean Institute of Traditional Landscape Architecture
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• v.37 no.3
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• pp.62-82
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• 2019

Taking the three Wuyi-Gugok Drawings, 『A Picture Showing the Boundary Between Mountains and Rivers: A』, 『Landscape of the Jiuqu River in the Wuyi Mountain: B』 and 『Eighteen Sceneries of Wuyi Mountain: C』, which were produced in the mid-Qing Dynasty as the research objects and after investigating the names recorded in the paintings, this paper tries to analyze the scenic spots, scene types and images in the literature survey. Also, based on the number of Scenic type and the number of Scenic name in each Gok, landscape richness(LR) and landscape similarity(LS) of the Gugok scenic spots, the cognitive characteristics of the landscape in the 18th century were carefully observed. The results are as follows. Firstly, according to the description statistics of scenic spot types in Wuyi Mountain Chronicle, there were 41 descriptions of scenery names in the three paintings, among which rock, peak and stone accounted for the majority. According to the data, the number of rocks, peaks and stones in Wuyi-Gugok landscape accounted for more than half, which reflected the characteristics of geological landscape such as Danxia landform in Wuyi-Gugok landscape. Secondly, the landscape of Gugok Stream(九曲溪) was diverse and full of images. The 1^st Gok Daewangbong(大王峰) and Manjeongbong(幔亭峰), the 2^nd Gok Oknyeobong(玉女峰), the 3^rd Gok Sojangbong(小藏峰), the 4^th Gok Daejangbong(大藏峰), the 5^th Gok Daeeunbyeong(大隱屛) and Muijeongsa(武夷精舍), the 6^th Gok Seonjangbong(仙掌峰) and Cheonyubong(天游峰) all had outstanding landscape in each Gok. However, the landscape features of the 7^th~9^th Gok were relatively low. Thirdly, according to the landscape image survey of each Gok, the image formation of Gugok cultural landscape originates from the specificity of the myths and legends related to Wuyi Mountain, and the landscape is highly well-known. Due to the specificity, the landscape recognition was very high. In particular, the 1^st Gok and the 5^th Gok closely related to the Taoist culture based on Muigun, the Stone Carving culture and the Boat Tour culture related to neo-confucianism culture of Zhu Xi. Fourthly, according to the analysis results of landscape similarity of 41 landscape types shown in the figure, the similarity of A and C was very high. The morphological description and the relationship of distant and near performance was very similar. Therefore, it could be judged that this was obviously influenced by one painting. As a whole, the names of the scenes depicted in the three paintings were formed at least in the first half of 18^th century through a long history of inheritance, accumulated myths and legends, and the names of the scenes. The order of the scenery names in three Drawings had some differences. But among the scenery names appearing in all three Drawings, there were 21 stones, 20 rocks and 17 peaks. Stones, rocks and peaks guided the landscape of Gugok Streams in Wuyi Mountain. Fifthly, Seonjodae(仙釣臺) in A and C was described in the 4^th Gok, but what deserved attention was that it was known as the scenery name of the 3^rd Gok in Korean. In addition, Seungjindong(升眞洞) in the 1^st Gok and Seokdangsa(石堂寺) in the 7^th Gok were not described in Drawings A, B and C. This is a special point that needs to be studied in the future.

• Journal of Intelligence and Information Systems
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• v.19 no.1
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• pp.57-77
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• 2013

Social media has become one of the most popular media in web and mobile application. In 2011, social networks and blogs are still the top destination of online users, according to a study from Nielsen Company. In their studies, nearly 4 in 5active users visit social network and blog. Social Networks and Blogs sites rule Americans' Internet time, accounting to 23 percent of time spent online. Facebook is the main social network that the U.S internet users spend time more than the other social network services such as Yahoo, Google, AOL Media Network, Twitter, Linked In and so on. In recent trend, most of the companies promote their products in the Facebook by creating the "Facebook Page" that refers to specific product. The "Like" option allows user to subscribed and received updates their interested on from the page. The film makers which produce a lot of films around the world also take part to market and promote their films by exploiting the advantages of using the "Facebook Page". In addition, a great number of streaming service providers allows users to subscribe their service to watch and enjoy movies and TV program. They can instantly watch movies and TV program over the internet to PCs, Macs and TVs. Netflix alone as the world's leading subscription service have more than 30 million streaming members in the United States, Latin America, the United Kingdom and the Nordics. As the matter of facts, a million of movies and TV program with different of genres are offered to the subscriber. In contrast, users need spend a lot time to find the right movies which are related to their interest genre. Recent years there are many researchers who have been propose a method to improve prediction the rating or preference that would give the most related items such as books, music or movies to the garget user or the group of users that have the same interest in the particular items. One of the most popular methods to build recommendation system is traditional Collaborative Filtering (CF). The method compute the similarity of the target user and other users, which then are cluster in the same interest on items according which items that users have been rated. The method then predicts other items from the same group of users to recommend to a group of users. Moreover, There are many items that need to study for suggesting to users such as books, music, movies, news, videos and so on. However, in this paper we only focus on movie as item to recommend to users. In addition, there are many challenges for CF task. Firstly, the "sparsity problem"; it occurs when user information preference is not enough. The recommendation accuracies result is lower compared to the neighbor who composed with a large amount of ratings. The second problem is "cold-start problem"; it occurs whenever new users or items are added into the system, which each has norating or a few rating. For instance, no personalized predictions can be made for a new user without any ratings on the record. In this research we propose a clustering method according to the users' genre interest extracted from social network service (SNS) and user's movies rating information system to solve the "cold-start problem." Our proposed method will clusters the target user together with the other users by combining the user genre interest and the rating information. It is important to realize a huge amount of interesting and useful user's information from Facebook Graph, we can extract information from the "Facebook Page" which "Like" by them. Moreover, we use the Internet Movie Database(IMDb) as the main dataset. The IMDbis online databases that consist of a large amount of information related to movies, TV programs and including actors. This dataset not only used to provide movie information in our Movie Rating Systems, but also as resources to provide movie genre information which extracted from the "Facebook Page". Formerly, the user must login with their Facebook account to login to the Movie Rating System, at the same time our system will collect the genre interest from the "Facebook Page". We conduct many experiments with other methods to see how our method performs and we also compare to the other methods. First, we compared our proposed method in the case of the normal recommendation to see how our system improves the recommendation result. Then we experiment method in case of cold-start problem. Our experiment show that our method is outperform than the other methods. In these two cases of our experimentation, we see that our proposed method produces better result in case both cases.