• Journal of Intelligence and Information Systems
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• v.24 no.4
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• pp.33-49
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• 2018

The impact of TV Drama success on TV Rating and the channel promotion effectiveness is very high. The cultural and business impact has been also demonstrated through the Korean Wave. Therefore, the early prediction of the blockbuster success of TV Drama is very important from the strategic perspective of the media industry. Previous studies have tried to predict the audience ratings and success of drama based on various methods. However, most of the studies have made simple predictions using intuitive methods such as the main actor and time zone. These studies have limitations in predicting. In this study, we propose a model for predicting the popularity of drama by analyzing the customer's viewing pattern based on various theories. This is not only a theoretical contribution but also has a contribution from the practical point of view that can be used in actual broadcasting companies. In this study, we collected data of 280 TV mini-series dramas, broadcasted over the terrestrial channels for 10 years from 2003 to 2012. From the data, we selected the most highly ranked and the least highly ranked 45 TV drama and analyzed the viewing patterns of them by 11-step. The various assumptions and conditions for modeling are based on existing studies, or by the opinions of actual broadcasters and by data mining techniques. Then, we developed a prediction model by measuring the viewing-time distance (difference) using Euclidean and Correlation method, which is termed in our study similarity (the sum of distance). Through the similarity measure, we predicted the success of dramas from the viewer's initial viewing-time pattern distribution using 1~5 episodes. In order to confirm that the model is shaken according to the measurement method, various distance measurement methods were applied and the model was checked for its dryness. And when the model was established, we could make a more predictive model using a grid search. Furthermore, we classified the viewers who had watched TV drama more than 70% of the total airtime as the "passionate viewer" when a new drama is broadcasted. Then we compared the drama's passionate viewer percentage the most highly ranked and the least highly ranked dramas. So that we can determine the possibility of blockbuster TV mini-series. We find that the initial viewing-time pattern is the key factor for the prediction of blockbuster dramas. From our model, block-buster dramas were correctly classified with the 75.47% accuracy with the initial viewing-time pattern analysis. This paper shows high prediction rate while suggesting audience rating method different from existing ones. Currently, broadcasters rely heavily on some famous actors called so-called star systems, so they are in more severe competition than ever due to rising production costs of broadcasting programs, long-term recession, aggressive investment in comprehensive programming channels and large corporations. Everyone is in a financially difficult situation. The basic revenue model of these broadcasters is advertising, and the execution of advertising is based on audience rating as a basic index. In the drama, there is uncertainty in the drama market that it is difficult to forecast the demand due to the nature of the commodity, while the drama market has a high financial contribution in the success of various contents of the broadcasting company. Therefore, to minimize the risk of failure. Thus, by analyzing the distribution of the first-time viewing time, it can be a practical help to establish a response strategy (organization/ marketing/story change, etc.) of the related company. Also, in this paper, we found that the behavior of the audience is crucial to the success of the program. In this paper, we define TV viewing as a measure of how enthusiastically watching TV is watched. We can predict the success of the program successfully by calculating the loyalty of the customer with the hot blood. This way of calculating loyalty can also be used to calculate loyalty to various platforms. It can also be used for marketing programs such as highlights, script previews, making movies, characters, games, and other marketing projects.

• Korean Journal of Agricultural Science
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• v.4 no.2
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• pp.344-390
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• 1977

The purpose of this paper was to schedule optimum cutting strategy which could maximize the total yield under certain restrictions on periodic timber removals and harvest areas from an industrial forest, based on a linear programming technique. Sensitivity of the regulation model to variations in restrictions has also been analyzed to get information on the changes of total yield in the planning period. The regulation procedure has been made on the experimental forest of the Agricultural College of Seoul National University. The forest is composed of 219 cutting units, and characterized by younger age group which is very common in Korea. The planning period is devided into 10 cutting periods of five years each, and cutting is permissible only on the stands of age groups 5-9. It is also assumed in the study that the subsequent forests are established immediately after cutting existing forests, non-stocked forest lands are planted in first cutting period, and established forests are fully stocked until next harvest. All feasible cutting regimes have been defined to each unit depending on their age groups. Total yield (Vi, k) of each regime expected in the planning period has been projected using stand yield tables and forest inventory data, and the regime which gives highest Vi, k has been selected as a optimum cutting regime. After calculating periodic yields and cutting areas, and total yield from the optimum regimes selected without any restrictions, the upper and lower limits of periodic yields(Vj-max, Vj-min) and those of periodic cutting areas (Aj-max, Aj-min) have been decided. The optimum regimes under such restrictions have been selected by linear programming. The results of the study may be summarized as follows:- 1. The fluctuations of periodic harvest yields and areas under cutting regimes selected without restrictions were very great, because of irregular composition of age classes and growing stocks of existing stands. About 68.8 percent of total yield is expected in period 10, while none of yield in periods 6 and 7. 2. After inspection of the above solution, restricted optimum cutting regimes were obtained under the restrictions of Amin=150 ha, Amax=400ha, $Vmin=5,000m^3$ and $Vmax=50,000m^3$, using LP regulation model. As a result, about $50,000m^3$ of stable harvest yield per period and a relatively balanced age group distribution is expected from period 5. In this case, the loss in total yield was about 29 percent of that of unrestricted regimes. 3. Thinning schedule could be easily treated by the model presented in the study, and the thinnings made it possible to select optimum regimes which might be effective for smoothing the wood flows, not to speak of increasing total yield in the planning period. 4. It was known that the stronger the restrictions becomes in the optimum solution the earlier the period comes in which balanced harvest yields and age group distribution can be formed. There was also a tendency in this particular case that the periodic yields were strongly affected by constraints, and the fluctuations of harvest areas depended upon the amount of periodic yields. 5. Because the total yield was decreased at the increasing rate with imposing stronger restrictions, the Joss would be very great where strict sustained yield and normal age group distribution are required in the earlier periods. 6. Total yield under the same restrictions in a period was increased by lowering the felling age and extending the range of cutting age groups. Therefore, it seemed to be advantageous for producing maximum timber yield to adopt wider range of cutting age groups with the lower limit at which the smallest utilization size of timber could be produced. 7. The LP regulation model presented in the study seemed to be useful in the Korean situation from the following point of view: (1) The model can provide forest managers with the solution of where, when, and how much to cut in order to best fulfill the owners objective. (2) Planning is visualized as a continuous process where new strateges are automatically evolved as changes in the forest environment are recognized. (3) The cost (measured as decrease in total yield) of imposing restrictions can be easily evaluated. (4) Thinning schedule can be treated without difficulty. (5) The model can be applied to irregular forests. (6) Traditional regulation methods can be rainforced by the model.