• Journal of the Korean Institute of Landscape Architecture
• /
• v.42 no.5
• /
• pp.52-63
• /
• 2014

This is the study on users' perception gap about the effects of leisure space development of the Han River Citizen Park according to leisure constraints and was to identify leisure constraints expressed when using Han River Citizen Park and identify perception on positive and negative impacts according to use behavior characteristics of Han River Citizen Park and leisure space development. In order to compare and analyze the perception gap about the leisure space development of Han River Citizen Park users according to leisure constraints, we classified information and resources, economic affordability, free time on leisure activities in Han River Citizen Park into high influence group and low influence group and did an in-depth analysis. The results are as follows. First, Han River Citizen Park has the nature of neighborhood use with little influence on leisure constraints such as jogging and marathon, biking etc. including walking and relaxation and was a place to visit for simple exercises. Second, in the effects according to leisure space development of Han River Citizen Park, affordable leisure activity costs and ease of access were evaluated the most highly and congestion due to increasing users, increase in administrative operating budget and management personnel were concerned the most. Third, the leisure constraints in Han River Citizen Park were affected in the order of free time, economic affordability, information and resources. There were also perception gaps in congestion due to an increase in administrative operating budget and management personnel, increase in users including improvement of various leisure opportunities and leisure levels, encouragement of pride and attachment for Han river, affordable leisure activity costs depending on the degree of the influence of leisure constraints. Therefore, this study can be said to have the meaning in that we could identify leisure constraints affecting Han River Citizen Park users and resulting perception in leisure space development and revealed that the degree of the influence of leisure constraints varies in the use behavior of leisure space development of Han River Citizen Park and perception of positive and negative development impact. In addition, in order to resolve leisure complaints according to leisure constraints, we studied the need of accompaniment of leisure space operation and management system, development of various customized programs, introduction of recreational space and facilities prioritizing public interest rather than private interest with public relations and information delivery about leisure space of Han River Citizen Park.

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

• Asia pacific journal of information systems
• /
• v.17 no.4
• /
• pp.187-206
• /
• 2007

The development of computer and information technology has been combined with the information superhighway internet infrastructure, so information widely spreads not only in special fields but also in the daily lives of people. Information ubiquity influences the traditional way of transaction, and leads a new E-commerce which distinguishes from the existing E-commerce. Not only goods as physical but also service as non-physical come into E-commerce. As the scale of E-Commerce is being enlarged as well. It keeps people from finding information they want. Recommender systems are now becoming the main tools for E-Commerce to mitigate the information overload. Recommender systems can be defined as systems for suggesting some Items(goods or service) considering customers' interests or tastes. They are being used by E-commerce web sites to suggest products to their customers who want to find something for them and to provide them with information to help them decide which to purchase. There are several approaches of recommending goods to customer in recommender system but in this study, the main subject is focused on collaborative filtering technique. This study presents a possibility of pre-evaluation for the prediction performance of customer's preference in collaborative filtering before the process of customer's preference prediction. Pre-evaluation for the prediction performance of each customer having low performance is classified by using the statistical features of ratings rated by each customer is conducted before the prediction process. In this study, MovieLens 100K dataset is used to analyze the accuracy of classification. The classification criteria are set by using the training sets divided 80% from the 100K dataset. In the process of classification, the customers are divided into two groups, classified group and non classified group. To compare the prediction performance of classified group and non classified group, the prediction process runs the 20% test set through the Neighborhood Based Collaborative Filtering Algorithm and Correspondence Mean Algorithm. The prediction errors from those prediction algorithm are allocated to each customer and compared with each user's error. Research hypothesis : Two research hypotheses are formulated in this study to test the accuracy of the classification criterion as follows. Hypothesis 1: The estimation accuracy of groups classified according to the standard deviation of each user's ratings has significant difference. To test the Hypothesis 1, the standard deviation is calculated for each user in training set which is divided 80% from MovieLens 100K dataset. Four groups are classified according to the quartile of the each user's standard deviations. It is compared to test the estimation errors of each group which results from test set are significantly different. Hypothesis 2: The estimation accuracy of groups that are classified according to the distribution of each user's ratings have significant differences. To test the Hypothesis 2, the distributions of each user's ratings are compared with the distribution of ratings of all customers in training set which is divided 80% from MovieLens 100K dataset. It assumes that the customers whose ratings' distribution are different from that of all customers would have low performance, so six types of different distributions are set to be compared. The test groups are classified into fit group or non-fit group according to the each type of different distribution assumed. The degrees in accordance with each type of distribution and each customer's distributions are tested by the test of ${\chi}^2$ goodness-of-fit and classified two groups for testing the difference of the mean of errors. Also, the degree of goodness-of-fit with the distribution of each user's ratings and the average distribution of the ratings in the training set are closely related to the prediction errors from those prediction algorithms. Through this study, the customers who have lower performance of prediction than the rest in the system are classified by those two criteria, which are set by statistical features of customers ratings in the training set, before the prediction process.

• Korean Journal of Social Welfare
• /
• v.60 no.2
• /
• pp.99-129
• /
• 2008

This study analyzed the resilience process of persons with disabilities using the grounded theory approach. To conduct this study, the researcher conducted in-depth interviews with 8 persons with disabilities. In data analysis, this study identified 393 concepts on the resilience process of persons with disabilities and the concepts were categorized into 45 sub-categories and 18 primary categories. In the paradigm model on the resilience process of persons with disabilities, it was identified that casual conditions included 'unawareness of disability before being disability', 'extreme pain', 'repressing psychological pain', and the contingent conditions were 'dis-empowerment by staying in home', 'isolation by himself with difficulty in accepting the disability', 'experience of frustration from social barriers with prejudice against persons with disabilities'. Also, it was identified that the resilience process could be dependent on the type and the degree of the disability, the gender, and the length of time being disability. In spite of the casual and contingent conditions, the central way in which persons with disabilities could acquire resilience was identified as 'enhancement of the power of positive thinking'. The control conditions which accelerate or retard central phenomenon were 'the awareness of not being alone through family, friends, neighborhood and the social system' externally and 'finding purpose in life through religion and help from other persons with disabilities', internally. The action/interactional sequences enhanced the efforts, self searching and active acting, and as a result, persons with disabilities could find comfort in life, participate in society and change the perspective of disability in society. The core categories of resilience process in persons with disabilities were a belief in affirmation and choice of life by initiative. In the process analysis, stages developed in the following: 'pain', 'strangeness', 'reflection', 'daily life'. This stage was more continuous and causal than discrete and complete. In this process, the types of resilience of persons with disabilities are divided into 'existence reflection', 'course development', 'implicit endeavor', and 'active execution'. This study showed the details of the paradigm models, the process and types with an in-depth understanding of the resilience process of persons with disabilities using grounded theory as well as theory construction and policy and clinical involvement on the study of persons with disabilities.

• Journal of Life Science
• /
• v.26 no.8
• /
• pp.904-910
• /
• 2016

Complex traits are determined by the combined effects of many loci and are affected by gene networks or biological pathways. Systems biology approaches have an important role in the identification of candidate genes related to complex diseases or traits at the system level. The gene network analysis has been performed by diverse types of methods such as gene co-expression, gene regulatory relationships, protein-protein interaction (PPI) and genetic networks. Moreover, the network-based methods were described for predicting gene functions such as graph theoretic method, neighborhood counting based methods and weighted function. However, there are a limited number of researches in livestock. The present study systemically analyzed genes associated with 102 types of economic traits based on the Animal Trait Ontology (ATO) and identified their relationships based on the gene co-expression network and PPI network in cattle. Then, we constructed the two types of gene network databases and network visualization system (http://www.nabc.go.kr/cg). We used a gene co-expression network analysis from the bovine expression value of bovine genes to generate gene co-expression network. PPI network was constructed from Human protein reference database based on the orthologous relationship between human and cattle. Finally, candidate genes and their network relationships were identified in each trait. They were typologically centered with large degree and betweenness centrality (BC) value in the gene network. The ontle program was applied to generate the database and to visualize the gene network results. This information would serve as valuable resources for exploiting genomic functions that influence economically and agriculturally important traits in cattle.