• The Journal of the Korea Contents Association
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• v.16 no.9
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• pp.386-395
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• 2016

The aim of the study was to explore the effects of parental media mediation types on adolescents' perception of the usefulness of the broadcasting rating system. A total of 520 middle school students participated in the survey. Results showed a higher utility of 'autonomy-supportive restriction' strategy by the parents led to more positive perception of the usefulness of the rating system. This study argues that parents need to pursue a mediation strategy in which a rationale for prohibiting media contents use should be provided and in which the perspective of the adolescent is taken seriously.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.4
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• pp.183-188
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• 2021

Collaborative filtering based recommender systems are currently indispensable function of commercial systems in various fields, being a useful service by providing customized products that users will prefer. However, there is a high possibility that the prediction of preferrable products is inaccurate, when the user's rating data are insufficient. In order to overcome this drawback, this study suggests a stepwise method for prediction of product ratings. If the application conditions of the prediction method corresponding to each step are not satisfied, the method of the next step is applied. To evaluate the performance of the proposed method, experiments using a public dataset are conducted. As a result, our method significantly improves prediction and precision performance of collaborative filtering systems employing various conventional similarity measures and outperforms performance of the previous methods for solving rating data sparsity.

• The Journal of the Korea Contents Association
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• v.10 no.1
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• pp.256-266
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• 2010

This study is to suggest the solutions about the problems of maximum requirements of animation broadcasting. About 10 experts of broadcasting, animation production, animation related professor and researcher and government take part in this study and discuss problems and their solutions of maximum requirements of animation broadcasting from January to march in 2009. This policy introduced 4 years ago had the positive effects such as the increase of animation production and the number of animation production company, but also the negative effects such as continuous decrease of rating in terrestrial broadcasting and transferring to low rating time slot of animation, downward equalization of animation and changing to minimum securement method. This study suggested the solutions as follows; first the enlargement of this policy to cable and satellite broadcasting, second introducing incentive policy in prime time, third the subsidy of media oriented investment. Finally this study stressed that this solutions should not be compulsory order but voluntary system.

• The Journal of the Korea Contents Association
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• v.14 no.12
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• pp.598-606
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• 2014

While the United States and Japan have non-government film rating system, Korea and France are still maintaining governmental control process. But the restrict showing rate in Korea does possibly violate the Constitution with no theatre for the movies of that rate right now. No other visual media including broadcasting have any outer classification process before their showing. So we need to improve our system by replacing it with non-governmental system. To establish independent non-government rating system, first of all, the major companies of film industry should get together to set up Korean Classification and Rating Association, to support the Film Rating Board. The most important thing is that the board operates independently. Government can support art cinemas financially with rating fee. Juvenile protection groups have to keep watch on the process of the board going fairly as well. The chief obstacle for non-governmental rating system is the fact that major companies don't want to get it. But continuing efforts to find any rational way is worthy enough.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.5
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• pp.221-226
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• 2021

Collaborative filtering based recommender systems recommend user-preferrable items based on rating history and are essential function for the current various commercial purposes. In order to determine items to recommend, prediction of preference score for unrated items is estimated based on similar rating history. Previous studies usually employ two methods individually, i.e., similar user based or similar item based ones. These methods have drawbacks of degrading prediction accuracy in case of sparse user ratings data or when having difficulty with finding similar users or items. This study suggests a new rating prediction method by integrating the two previous methods. The proposed method has the advantage of consulting more similar ratings, thus improving the recommendation quality. The experimental results reveal that our method significantly improve the performance of previous methods, in terms of prediction accuracy, relevance level of recommended items, and that of recommended item ranks with a sparse dataset. With a rather dense dataset, it outperforms the previous methods in terms of prediction accuracy and shows comparable results in other metrics.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.12 no.2
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• pp.61-68
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• 2012

Recommender Systems have gained much popularity among researchers and is applied in a number of applications. The exponential growth of users and products poses some key challenges for recommender systems. Recommender Systems mostly suffer from scalability and accuracy. The accuracy of Recommender system is somehow inversely proportional to its scalability. In this paper we proposed a Context Aware Hybrid Recommender System using matrix reduction for Hybrid model and clustering technique for predication of item features. In our approach we used user item-feature rating, User Demographic information and context information i.e. specific time and day to improve scalability and accuracy. Our Algorithm produce better results because we reduce the dimension of items features matrix by using different reduction techniques and use user demographic information, construct context aware hybrid user model, cluster the similar user offline, find the nearest neighbors, predict the item features and recommend the Top N- items.

• Journal of the Korean Data and Information Science Society
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• v.21 no.1
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• pp.185-194
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• 2010

In recommender systems which are used widely at e-commerce, collaborative filtering needs the information of user-ratings and neighbor user-ratings. These are an important value for recommendation in recommender systems. We investigate the in-formation of rating in NBCFA (neighbor Based Collaborative Filtering Algorithm), we suggest new algorithm that improve prediction accuracy of recommender system. After we analyze relations between two variable and Error Value (EV), we suggest new algorithm and apply it to fitted line. This fitted line uses Least Squares Method (LSM) in Exploratory Data Analysis (EDA). To compute the prediction value of new algorithm, the fitted line is applied to experimental data with fitted function. In order to confirm prediction accuracy of new algorithm, we applied new algorithm to increased sparsity data and total data. As a result of study, the prediction accuracy of recommender system in the new algorithm was more improved than current algorithm.

• The Journal of the Korea Contents Association
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• v.16 no.11
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• pp.731-741
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• 2016

It has become five years since comprehensive programming licenses were rendered in Korea. Allocating a lion's share of their air time on live news and news commentaries, those channels have established a unique live production system or a broadcasting system which is heavily live production-oriented, to be exact. The live commentaries are filled with a mixture of news flashes, conventional news commentaries, and debates. Those channels get their news and commentary programs made through subsidiaries' where production directors and studio staffs belong. They, being very sensitive about viewer rating, tend to be aggressive about reruns of highly rated programs and they do not even seem to care when the regular programs actually went out. This kind of reckless strategy to pursue a higher viewer rating could limit not only new programming attempts but also exposure diversity.

• International journal of advanced smart convergence
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• v.9 no.3
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• pp.42-48
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• 2020

These days people are overwhelmed by information on the Internet thus searching for useful information becomes burdensome, often failing to acquire some in a reasonable time. Recommender systems are indispensable to fulfill such user needs through many practical commercial sites. This study proposes a novel similarity measure for user-based collaborative filtering which is a most popular technique for recommender systems. Compared to existing similarity measures, the main advantages of the suggested measure are that it takes all the ratings given by users into account for computing similarity, thus relieving the inherent data sparsity problem and that it reflects the uncertainty or vagueness of user ratings through fuzzy logic. Performance of the proposed measure is examined by conducting extensive experiments. It is found that it demonstrates superiority over previous relevant measures in terms of major quality metrics.

• Journal of the Korean Data and Information Science Society
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• v.21 no.4
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• pp.803-811
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• 2010

This study analyzes the characteristics of preference ratings by dividing estimated values into four groups according to rank correlation coefficient after obtaining preference estimated value to user's ratings by using collaborative filtering algorithm. It is known that the value of standard error of skewness and standard error of kurtosis lower in the group of higher rank correlation coefficient This explains that the preference of higher rank correlation coefficient has lower extreme values and the differences of preference rating values. In addition, top n recommendation lists are made after obtaining rank fitting by using the result ranks of prediction value and the ranks of real rated values, and this top n is applied to the four groups. The value of top n recommendation is calculated higher in the group of higher rank correlation coefficient, and the recommendation accuracy in the group of higher rank correlation coefficient is higher than that in the group of lower rank correlation coefficient Thus, when using standard error of skewness and standard error of kurtosis in recommender system, rank correlation coefficient can be higher, and so the accuracy of recommendation prediction can be increased.