• Journal of the Korean housing association
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• v.22 no.1
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• pp.57-64
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• 2011

The purpose of this study is to investigate design elements to strengthen apartment communities in territoriality perspectives. To this end, the study first identified the functions of territoriality to communities, and examined precedent studies on community planning. Then, looking into actual apartment, the study analyzed design elements of the apartments by territoriality functions, and found significant community-building and community-strengthening elements. The results of case studies are as follows: Specific design elements are 1) both individual and shared places from the perspective of possession, 2) both physical boundaries including visible mark and physical demarcation, and emotional boundaries including space arrangement and visual expansion from the perspective of boundary-regulation, 3) both physical proximity, such as providing space for physical encounter and easy access, and emotional proximity such as homogeneity and intimacy from the perspective of proximity 4) design differentiation and adoption of the design concept to express the identity of the community from the perspective of proximity personalization.

• Proceeding of Spring/Autumn Annual Conference of KHA
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• 2009.11a
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• pp.17-22
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• 2009

This research was conducted to investigate methods to strengthen the apartment community from the territoriality point of view. In order to achieve this goal, first of all the concept and the structure of territoriality are studied through articles and literature reviews to ensure a full understanding of the community strengthening-related plan elements suggested in the previous research. The study is then divided into the categories of semi-private territory and semi-public territory to pursue analysis based on the issues related to strengthening community such as possession, proximity, defense, boundary-regulation, and personalization in case by case studies. The results of case studies are as follows: in the case of possession with psychological concept of possession, the extension of private space and exclusive space are suggested. And for the concept of proximity, that is, the physical and psychological distance for sense of the same nature, increased opportunity of meeting the residents in the same building or same area are suggested And for the psychological proximity, familiar design and facilities for meeting and exchange-related methods are shown. For the case of indirect defense in defense concept, the methods related to monitoring and supervising facilities for protection are found. For the boundary-regulations, it is based on the concepts of physical and psychological borderlines; walls either vertical or horizontal, or such obstacle-related methods are suggested for the physical borderline, and space structure and symbols-related methods are found for psychological borderline. Finally, for the case of personalization which is related to the expression of identity, design-related methods are suggested.

• Journal of KIISE
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• v.41 no.12
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• pp.1117-1125
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• 2014

Folksonomy data, which is derived from social tagging systems, is a useful source for understanding a user's intention and interest. Using the folksonomy data, it is possible to create an accurate user profile which can be utilized to build a personalized search system. However there are limitations in some of the traditional methods such as Vector Space Model(VSM) for user profiling and similarity computation. This paper suggests a novel method with graph-based user and document profile which uses the proximity information of query terms to improve personalized search. We demonstrate the performance of the suggested method by comparing its performance with several state-of-the-art VSM based personalization models in two different folksonomy datasets. The results show that the proposed model constantly outperforms the other state-of-the-art personalization models. Furthermore, the parameter sensitivity results show that the proposed model is parameter-free in that it is not affected by the idiosyncratic nature of datasets.

• Journal of Digital Convergence
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• v.14 no.8
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• pp.57-67
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• 2016

As the Internet is becoming more socialized, Korean government, publishing a slogan, Government 3.0, has recently began to find a way to deliver its administration services to the public in more personalized manner. Policy directions to implement this advanced idea, are however still at large, primarily because of the vague nature of 'personalized'. This study, therefore, explores the possibility of getting public administrative services closer to personalization. To achieve this objective, this study attempts to develop a integrative framework of classifying the administration services to the public, based on two dimensions - the degree of citizen-oriented and the degree of government-driven, both of which are perhaps key determinants of personaliztion of services. For each quadrant of the framework, key features, characteristics, and conditions to be met are explained and followed by exemplary cases and policy implications.

• 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.