• The Korean Journal of Applied Statistics
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• v.33 no.6
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• pp.833-842
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• 2020

Global platforms such as Netflix, Amazon, and YouTube have developed a precise recommendation system based on various information from large set of customers and many of the items recommended here are leading to actual purchases. In this paper, a cluster analysis was conducted according to the attribute of the content, expecting that there would be a difference in user preferences according to the attribute of the recommended content. Gower distance was used for use regardless of the type of variables. In this paper, using the data of movie rating site 'The Movie Dataset', the users were grouped hierarchically and recommended movies based on genre, director and actor variables. To evaluate the recommended systems proposed, user group was divided into train set and test set to examine the precision. The results showed that proposed algorithms have far higher precision than UBCF.

• Proceedings of the Korea Contents Association Conference
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• 2007.11a
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• pp.238-242
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• 2007

The existent UCC sites display only finalized contents by publisher. It dose not provide any platform for resources of UCC that public users could reorganize. This paper has developed a platform to be able to produce reusable content using the scenes of the contents and produced a 3D animation with multiple story. It is necessary for user to search the provided contents for easy reorganization of the contents. The scene is classified by the description and information of the scene for handy search. It is obscure for a movie clip to be represent with only one word. Therefore, the proposed platform provides the search technique with a overlapping choice for the specific categories that include most of elements for the scene. Then the user can choose a specific range of the selected movie clip, make a new story with reorganizing the order, and put a caption or BGM on the movie clip. The complete movie clip has the search preferences as a category, new clips, and top favorites. With the Multi-Story line concept, we made a 3D animation about episodes of thermal dolls in the Doll World. This attempt will come to the new marketing way for a field of the visual media like as Music Video, Drama, Feature Film, Commercial Film.

• Korean Management Science Review
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• v.32 no.2
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• pp.125-143
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• 2015

When studying the film industry, researchers have seldom addressed the dynamic interaction between marketing information and word of mouth in the motion picture industry mainly because of the limitation of traditional research methodologies. This study explores integration and competition among important variables influencing on audience's choice on movie selection, particularly by using a new method of agent-based modeling including competitive environment. Decision process of moviegoer composed of transition probability based on multinomial logit model, considering marketing and box-office information, critique, and word of mouth from other moviegoers. After validating the fitness of market share among released movies, this study conducted a set of simulation experiments considering several variables such as market size, change of weight between variables, and movie performance under competition. Propositions are derived from the simulation results is also suggested for future research.

• Proceedings of the Korea Information Processing Society Conference
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• 2004.05a
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• pp.43-46
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• 2004

웹상에서 제공되는 영상정보 자원의 양이 급증하고 이용자들의 문화적인 수요가 높아진 것에 비해 영상정보를 포괄적으로 기술할 수 있는 체계적인 서지기술은 부족한 실정이다. 기존의 영상정보 기술 메타데이터 요소로는 이용자가 탐색하고자 하는 정보자원을 적절하게 검색하거나 혹은 검색된 자원 중에서 서로 연관성이 있는 정보를 그룹화하여 보여주는 것이 불가능하다[1]. 따라서 영상정보 자원이 지니는 다양한 수준과 다양한 측면의 특성을 표현해 내기 위해 국내에서 제안된 영상정보 분야의 새로운 메타데이터 표준이 제시되었다. 본 논문에서는 제안된 영상정보 분야의 메타데이터를 기반으로 하는 영화 Context 검색 엔진을 개발하였고, 이에 따라 추후에 적절한 정보자원의 검색과 연관성 있는 정보의 그룹화로의 확장을 가능하게 할 것이다.

• Journal of Information Processing Systems
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• v.15 no.1
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• pp.127-136
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• 2019

User-based and item-based approaches have been developed as the solutions of the movie recommendation problem. However, the user-based approach is faced with the problem of sparsity, and the item-based approach is faced with the problem of not reflecting users' preferences. In order to solve these problems, there is a research on the combination of the two methods using the concept of similarity. In reality, it is not free from the problem of sparsity, since it has a lot of parameters to be calculated. In this study, we propose a combining method that simplifies the combination equation of prior study. This method is relatively free from the problem of sparsity, since it has less parameters to be calculated. Thus, it can get more accurate results by reflecting the users rating to calculate the parameters. It is very fast to predict new movie ratings as well. In experiments for the proposed method, the initial error is large, but the performance gets quickly stabilized after. In addition, it showed about 6% lower average error rate than the existing method using similarity.

• The Journal of the Korea institute of electronic communication sciences
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• v.14 no.2
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• pp.377-388
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• 2019

The system proposed in this paper was suggested as a big data system that works in the age of artificial intelligence of the 4th Industrial Revolution. The proposed system can be a good example in terms of government 's development of new intelligent big data information system. For example, the proposed system may be introduced into the system of a department as a function of the integration of existing cinema ticket integration network or its networking. For this purpose, the proposed system transmits the user's profile to the film producer or other company, where it is provided as comparison data. Soon, the information is sent to the user-specific characteristic data and then the film-maker will be able to gauge the success of the three elements of the movie's performance, cinematic quality, and break-even point in real time, which are revealed through the movie review that the actual user feels, including the so-called 'new reinterpretation.

• Journal of Internet Computing and Services
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• v.18 no.3
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• pp.49-61
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• 2017

Wikipedia infoboxes have emerged as an important structured information source on the web. To compose infobox for an article, considerable amount of manual effort is required from an author. Due to this manual involvement, infobox suffers from inconsistency, data heterogeneity, incompleteness, schema drift etc. Prior works attempted to solve those problems by generating infobox automatically based on the corresponding article text. However, there are many articles in Wikipedia that do not have enough text content to generate infobox. In this paper, we present an automated approach to generate infobox for movie domain of Wikipedia by extracting information from several sources of the web instead of relying on article text only. The proposed methodology has been developed using semantic relations of article content and available semi-structured information of the web. It processes the article text through some classification processes to identify the template from the large pool of template list. Finally, it extracts the information for the corresponding template attributes from web and thus generates infobox. Through a comprehensive experimental evaluation the proposed scheme was demonstrated as an effective and efficient approach to generate Wikipedia infobox.

• Information Systems Review
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• v.21 no.2
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• pp.29-50
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• 2019

Social media is divided into Owned Media, operated by companies according to information sources, and Earned Media, which third parties produce contents. Social media research developing the logic that brand-related content in social media increases awareness of potential customers and positively changes brand attitudes, resulting in increased sales and business performance. However, there are limitations in previous researches that can not fully explain the difference of media synergy effect according to the information source of social media. it is very important for the consumer to integrate media management because consumers are more likely to choose appropriate media information for the information needed at each decision making stage. The purpose of this study is to analyze the effect of eWOM of review site and social media (owned media and earned media) on movie sales. To do this, we collected 3,589 review data from films released in 2017. The results of the study showed that eWOM of review site, social media (owned media and earned media) had a positive effect on movie sales. However, it was found that the effect of moderating eWOM of review site was different between the owned media and the earend media.

• Applied Microscopy
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• v.40 no.1
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• pp.47-51
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• 2010

The capability of real-time observation in TEM is quite useful to study dynamic phenomena of materials in a certain variable ambience. In performing the experiment, the choice of video recording program is an important factor to obtain high quality of movie streaming. Window Movie Maker (WMM) is generally recommended as a default video recording program if one uses "DV Capture" function in DigitalMicrograph$^{TM}$ (DM) software. However, the image quality does not often satisfy the condition for high-resolution microscopic analysis since the severe information loss in the final result occurs during the conversion process. As a good candidate to overcome this problem, Virtual-Dub is highly recommended since the information loss can be minimized through the streaming process. In this report, we demonstrated how useful VirtualDub works in a high-resolution movie recording. Quantitative comparison of the information quality between the images recorded by each software, WMM and VirtualDub, was carried out based on histogram analysis. As a result, the image recorded by VirtualDub was improved ~13% in brightness and ~122% in contrast compared with the image obtained by WMM at the same imaging condition. Remarkably, the gray gradation (meaning an amount of information) becomes wider up to ~115% than that of the WMM result.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2007.05a
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• pp.218-225
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• 2007

Recommendation system is one way of implementing personalized service. The collaborative filtering is one of the major techniques that have been employed for recommendation systems. It has proven its effectiveness in the recommendation systems for such domain as motion picture or music. However, it has some limitations, i.e., sparsity and scalability. In this research, as one way of overcoming such limitations, we proposed the stepwise collaborative filtering method. To show the practicality of our proposed method, we designed and implemented a movie recommendation system which we shall call Step_CF, and its performance was evaluated using MovieLens data. The performance of Step_CF was better than that of Basic_CF that was implemented using the original collaborative filtering method.