• Korean Journal of Culture and Social Issue
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• v.24 no.2
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• pp.273-300
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• 2018

In order to analyze the experience of women from the provinces settling in Seoul, the current study conducts in-depth interviews on seven women who are working in Seoul after coming up to Seoul to attend and graduate from university. The results of the interview were analyzed using grounded theory methodology, under which the open coding extracted one hundred ten concepts, twenty-one sub-categories, and eleven categories encompassing them all. Causal condition is 'difficulty of settling in Seoul' and contextual conditions are 'intensification of psychological anxiety', 'reduction in quality of life', and 'continuation of economic instability'. The central phenomenon is 'dilemma of continuing life in Seoul' and intervening condition is 'diagnosis of ten years after coming up to Seoul'. Action/interaction strategies are 'changes in personal life', 'securing economic abilities through a stable job', and 'finding ways to participate in the society', while the result was 'choosing whether to continue living in Seoul'. The paradigm of experience of women from the provinces settling in Seoul proceed from coming up to Seoul for university to becoming independent, adapting to life in the city, experiencing growth and failures, facing challenge and searching for solutions, and conducting self-evaluation and making new choices. The participants reported that they were aware of differences and experienced anxieties as a stranger in Seoul even after living in the city for ten years; the problems they face have become more complex and diverse since when they were in university, and while they launched a career and making money, the gap between them and their peers from Seoul has not closed. The women also express desperation that they may need to leave Seoul to find alternatives to problems caused by accumulated stress and social problems that cannot be solved by an individual. In conclusion, the current study confirmed that efforts by individuals can only have limited effects in helping women from the provinces to settle in Seoul, indicating that detailed policy plans are required to solve social issues in the overall Korean society.

• Journal of Intelligence and Information Systems
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• v.19 no.3
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• pp.25-44
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• 2013

Accessing movie contents has become easier and increased with the advent of smart TV, IPTV and web services that are able to be used to search and watch movies. In this situation, there are increasing search for preference movie contents of users. However, since the amount of provided movie contents is too large, the user needs more effort and time for searching the movie contents. Hence, there are a lot of researches for recommendations of personalized item through analysis and clustering of the user preferences and user profiles. In this study, we propose recommendation system which uses ontology based knowledge base. Our ontology can represent not only relations between metadata of movies but also relations between metadata and profile of user. The relation of each metadata can show similarity between movies. In order to build, the knowledge base our ontology model is considered two aspects which are the movie metadata model and the user model. On the part of build the movie metadata model based on ontology, we decide main metadata that are genre, actor/actress, keywords and synopsis. Those affect that users choose the interested movie. And there are demographic information of user and relation between user and movie metadata in user model. In our model, movie ontology model consists of seven concepts (Movie, Genre, Keywords, Synopsis Keywords, Character, and Person), eight attributes (title, rating, limit, description, character name, character description, person job, person name) and ten relations between concepts. For our knowledge base, we input individual data of 14,374 movies for each concept in contents ontology model. This movie metadata knowledge base is used to search the movie that is related to interesting metadata of user. And it can search the similar movie through relations between concepts. We also propose the architecture for movie recommendation. The proposed architecture consists of four components. The first component search candidate movies based the demographic information of the user. In this component, we decide the group of users according to demographic information to recommend the movie for each group and define the rule to decide the group of users. We generate the query that be used to search the candidate movie for recommendation in this component. The second component search candidate movies based user preference. When users choose the movie, users consider metadata such as genre, actor/actress, synopsis, keywords. Users input their preference and then in this component, system search the movie based on users preferences. The proposed system can search the similar movie through relation between concepts, unlike existing movie recommendation systems. Each metadata of recommended candidate movies have weight that will be used for deciding recommendation order. The third component the merges results of first component and second component. In this step, we calculate the weight of movies using the weight value of metadata for each movie. Then we sort movies order by the weight value. The fourth component analyzes result of third component, and then it decides level of the contribution of metadata. And we apply contribution weight to metadata. Finally, we use the result of this step as recommendation for users. We test the usability of the proposed scheme by using web application. We implement that web application for experimental process by using JSP, Java Script and prot$\acute{e}$g$\acute{e}$ API. In our experiment, we collect results of 20 men and woman, ranging in age from 20 to 29. And we use 7,418 movies with rating that is not fewer than 7.0. In order to experiment, we provide Top-5, Top-10 and Top-20 recommended movies to user, and then users choose interested movies. The result of experiment is that average number of to choose interested movie are 2.1 in Top-5, 3.35 in Top-10, 6.35 in Top-20. It is better than results that are yielded by for each metadata.