• Journal of Digital Contents Society
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• v.17 no.5
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• pp.383-390
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• 2016

is a service providing story synopses that match user's query. This paper presents a classification of character types by clustering of character tendencies found in the user log of . We also present a visualization method of showing genre-action relationships to each character type, and investigate the genre-action relationships of the major character types. We found that a small number of character types can represent more than half of the character tendencies and the character types tend to have a relationship to particular genres and actions. According to this properties, it would be desirable to provide supports for creative writing classified by character types.

• Journal of KIISE:Software and Applications
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• v.30 no.3_4
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• pp.316-325
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• 2003

A user preference prediction method using an exiting collaborative filtering technique has used the nearest-neighborhood method based on the user preference about items and has sought the user's similarity from the Pearson correlation coefficient. Therefore, it does not reflect any contents about items and also solve the problem of the sparsity. This study suggests the preference prediction system using the similarity weight granted Bayesian estimated value and the associative user clustering to complement problems of an exiting collaborative preference prediction method. This method suggested in this paper groups the user according to the Genre by using Association Rule Hypergraph Partitioning Algorithm and the new user is classified into one of these Genres by Naive Bayes classifier to slove the problem of sparsity in the collaborative filtering system. Besides, for get the similarity between users belonged to the classified genre and new users, this study allows the different estimated value to item which user vote through Naive Bayes learning. If the preference with estimated value is applied to the exiting Pearson correlation coefficient, it is able to promote the precision of the prediction by reducing the error of the prediction because of missing value. To estimate the performance of suggested method, the suggested method is compared with existing collaborative filtering techniques. As a result, the proposed method is efficient for improving the accuracy of prediction through solving problems of existing collaborative filtering techniques.

• Journal of Korean Classical Literature and Education
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• no.34
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• pp.5-39
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• 2017

The purpose of this study is to establish genre knowledge that can be used to study classical Korean novels. Genre knowledge is important because it characterizes individual works based on their knowledge, as well as organically links them with the social context. In this study, I suggest that long classical Korean novels with similar contents can be analyzed using genre knowledge and analyze the "register" of the representative work Sohyeonsungrok as an example of a classical Korean novel. In the Sydney school, register connects genre and language. Register comprises field, tenor, and mode in the social context, and ideological meaning, interpersonal meaning, and textual meaning through language. These three meanings help us to understand how experiences are transformed into language, the relationship between participants, and the way a text is organized. Based on these frameworks, this study reveals that the linguistic features of Sohyeonsungrok is "an attitude that accepts a wide range of human emotions and desires and steadily waits for it to be included in norms." Classic Korean novels such as Sohyeonsungrok depict characters who are not wicked even though they cannot fully comply with social norms, and thereby create sympathy for family members living through Confucian ideology. This genre knowledge is useful for understanding the ideological implications of classical Korean novels.

• The KIPS Transactions:PartB
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• v.10B no.3
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• pp.287-296
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• 2003

There has been much research focused on collaborative filtering technique in Recommender System. However, these studies have shown the First-Rater Problem and the Sparsity Problem. The main purpose of this Paper is to solve these Problems. In this Paper, we suggest the user's predicting preference method using Bayesian estimated value and the associative user clustering for the recalculation of preference. In addition to this method, to complement a shortcoming, which doesn't regard the attribution of item, we use Representative Attribute-Neighborhood method that is used for the prediction when we find the similar neighborhood through extracting the representative attribution, which most affect the preference. We improved the efficiency by using the associative user's clustering analysis in order to calculate the preference of specific item within the cluster item vector to the collaborative filtering algorithm. Besides, for the problem of the Sparsity and First-Rater, through using Association Rule Hypergraph Partitioning algorithm associative users are clustered according to the genre. New users are classified into one of these genres by Naive Bayes classifier. In addition, in order to get the similarity value between users belonged to the classified genre and new users, and this paper allows the different estimated value to item which user evaluated through Naive Bayes learning. As applying the preference granted the estimated value to Pearson correlation coefficient, it can make the higher accuracy because the errors that cause the missing value come less. We evaluate our method on a large collaborative filtering database of user rating and it significantly outperforms previous proposed method.

• Proceedings of the Korean Society of Computer Information Conference
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• 2019.07a
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• pp.307-308
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• 2019

매치3 퍼즐 게임은 모바일 게임에서 많이 출시되고 있으며 많은 사람들이 즐기고 있는 장르이다. 또한 유사한 규칙과 형태를 가진 게임들이 지속적으로 출시 예정에 있다. 본 논문에서는 매치3 퍼즐 게임이 많은 레벨로 구성되어 있는 점에 비추어 매치3 퍼즐 게임의 레벨을 설계하는 방법에 대해서 후속 연구의 방향성을 제시한다.

• Proceedings of the Korean Society of Computer Information Conference
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• 2019.07a
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• pp.309-310
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• 2019

매치3 퍼즐 게임은 모바일 게임에서 많이 출시되고 있으며 많은 사람들이 즐기고 있는 장르이다. 또한 유사한 규칙과 형태를 가진 게임들이 지속적으로 출시 예정에 있다. 본 논문에서는 매치3 퍼즐 게임이 많은 레벨로 구성되어 있는 점에 비추어 매치3 퍼즐 게임의 레벨 난이도를 측정할 수 있는 방법에 대해서 후속 연구의 방향성을 제시한다.

• Korean journal of communication and information
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• v.46
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• pp.37-74
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• 2009

This research is an attempt to investigate cultural identity at the international level. Asia is one of the weakest communities in the world due to discrepancies in terms of political, economic, social and cultural aspects. Additionally, Asia has never been independent in communication flows since imperialist history until the Korean wave emerged at the turn of this century. The Korean wave reflects complex power embedded in postcolonial world in addition to cultural commonality among Asian audiences. I have conducted audience researches on Korean drama fandom in Japan and China. I adopt Lacanian psychoanalysis in order to interpret identity issues of Asian media audiences. Particularly, Deleuze and Guattari's theories are useful to scrutinize group identity of Asian community. Additionally, I refer to theories of nationalism.

• The Journal of the Korea Contents Association
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• v.10 no.1
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• pp.230-238
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• 2010

Since 1960, there has been growing recognition that the moving images reveal the 'realistic illusion(L'illusion r$\acute{e}$aliste)' rather than the reality itself and many theorists and film directors have tried to suggest the methodology to solve the problem of verisimilitude of the moving images. In this paper, we describe through the case analysis of the practical use of 'off-screen space' as a methodology which actualize the reality in documentaries, by minimizing the 'suture' effect which occurs the problem of verisimilitude, based on the theories of Bazin and Burch. We, consequently, believe that the application of the 'off-screen space' could be one of the appropriate possibility for the successful representation of reality in documentaries.

• Journal of Korea Game Society
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• v.21 no.4
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• pp.73-84
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• 2021

In this paper, we propose an analysis method that categorizes the Core that essential behaviors in game, the Primary that solves the game problem, and the Secondary that helps the Core and the Primary. The proposed method can analyze the genre similarity and characteristics of the game, the richness of the content, and the proficiency level of the game. case study were conducted to confirm whether the analysis items were consistent with the objective game experience. The results of this study are expected to be helpful in improving game design ability.

• Journal of Intelligence and Information Systems
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• v.29 no.1
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• pp.27-40
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• 2023

With the increase in the spread of smart devices and the impact of COVID-19, the consumption of media contents through smart devices has significantly increased. Along with this trend, the amount of media contents viewed through OTT platforms is increasing, that makes contents recommendations on these platforms more important. Previous contents-based recommendation researches have mostly utilized metadata that describes the characteristics of the contents, with a shortage of researches that utilize the contents' own descriptive metadata. In this paper, various text data including titles and synopses that describe the contents were used to recommend similar contents. KLUE-RoBERTa-large, a Korean language model with excellent performance, was used to train the model on the text data. A dataset of over 20,000 contents metadata including titles, synopses, composite genres, directors, actors, and hash tags information was used as training data. To enter the various text features into the language model, the features were concatenated using special tokens that indicate each feature. The test set was designed to promote the relative and objective nature of the model's similarity classification ability by using the three contents comparison method and applying multiple inspections to label the test set. Genres classification and hash tag classification prediction tasks were used to fine-tune the embeddings for the contents meta text data. As a result, the hash tag classification model showed an accuracy of over 90% based on the similarity test set, which was more than 9% better than the baseline language model. Through hash tag classification training, it was found that the language model's ability to classify similar contents was improved, which demonstrated the value of using a language model for the contents-based filtering.