• 한국컴퓨터정보학회논문지
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• 제14권2호
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• pp.221-229
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• 2009

인터넷과 정보통신 기술이 발전함에 따라 영화 유통의 창구도 다양화 되고 있으며, 인터넷 VOD 극장은 저렴한 가격과 인터넷이라는 시간과 공간의 제약성이 없는 특징을 통해 새로운 영화 창구로서 자리 매김 하고 있다. 본 연구에서는 인터넷 VOD 극장을 이용하는 데 있어서 기존 연구에서 나타난 기술수용이론의 지각된 유용성과 몰입이론의 변수 그리고 인터넷 VOD 극장에서 제공하는 영화에 대해 소비자가 느끼는 흘드백 기간에 대한 민감도와 인터넷 VOD 극장에서 제공되는 영화 장르 선택의 다양성이 소비자의 이용 의도에 미치는 영향에 대해 살펴보았다. 본 연구의 의의는 기존 오프라인 영화관 및 다른 영화매체와의 경쟁 속에서 새로운 유통 창구로 자리매김하고 있는 인터넷 VOD 극장의 매체적인 특성을 찾고, 인터넷 VOD 극장 이용자의 이용 의도에 영향을 미치는 요인을 분석함으로써 향후 인터넷 VOD 극장 수익 발전 방향을 모색했다는 점에서 실용적인 의미가 있을 것이다.

• Journal of Computing Science and Engineering
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• 제7권1호
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• pp.21-29
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• 2013

The introduction of recommender systems to existing online services is now practically inevitable, with the increasing number of items and users on online services. Popular recommender systems have successfully implemented satisfactory systems, which are usually based on collaborative filtering. However, collaborative filtering-based recommenders suffer from well-known problems, such as popularity bias, and the cold-start problem. In this paper, we propose an innovative collaborative-filtering based recommender system, which uses the concepts of Experts and Novices to create fine-grained recommendations that focus on being novel, while being kept relevant. Experts and Novices are defined using pre-made clusters of similar items, and the distribution of users' ratings among these clusters. Thus, in order to generate recommendations, the experts are found dynamically depending on the seed items of the novice. The proposed recommender system was built using the MovieLens 1 M dataset, and evaluated with novelty metrics. Results show that the proposed system outperforms matrix factorization methods according to discovery-based novelty metrics, and can be a solution to popularity bias and the cold-start problem, while still retaining collaborative filtering.

• 복식문화연구
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• 제28권6호
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• pp.830-844
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• 2020

The fourth Industrial Revolution, known as digital transformation, has made MZ generation to be the focus of the new consumer market, brought about the use of technological platforms a new consumption method. Currently, as various types of content collaboration are emerging that specifically targeting at the MZ generation. Content collaboration are considered an integration of content to create new values through co-existence and co-prosperity. This study identified the characteristics of collaboration of fashion brands from 2018 using literature and online news articles, and identified and classified through case studies of it determined movie content, game and virtual characters. By this research, it shown that collaboration with movie contents have increased the collaborative synergy by using the story in global media content. Collaboration with mobile games was generally used by young casual and sportswear brands. These brands which utilized characters from mobile games popular with to attract more teen consumers and strengthen brand awareness by adding values of high-technology and scarcity to the familiar images. In addition, collaboration with virtual characters has expanded value of the collaborative approach on expanding the range of advanced digital technology, from a promotional strategy during the distribution process through to the use of virtual models. As such, collaboration using the various types of content has developed beyond simple integration of identities among various areas, integrated products or brands that as a new value.

• 유통과학연구
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• 제20권6호
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• pp.21-32
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• 2022

Purpose: This study aims to test the influence of brand relationship, price, content, brand awareness, and electronic Word-Of-Mouth (eWOM) on willingness to pay for the subscription fee of Disney+ Hotstar. As the latest streaming service provider in Indonesia, Disney + Hotstar under Disney Media and Entertainment Distribution has actively conducted strategies to strengthen the brand and attract consumers. Research design, data and methodology: Structural Equation Modelling with WarpPLS approach was used to assess the proposed model gathering data from 316 people who have ever known about Disney+ Hotstar through an online survey using measurement items from previous literature. Results: Most responses were obtained from millennial generations. Findings demonstrated that brand relationships, price, content, and brand awareness positively influenced willingness to pay for the subscription fee whereas eWOM showed a negative and insignificant influence on the willingness to pay for the subscription fee. Conclusions: The most significant factor towards willingness to pay a for subscription fee is price, followed by brand awareness, brand relationship, and content. The result of this study may be used as a guide for professionals in the streaming service industry to better implement their strategies in influencing people to have the willingness to subscribe.

• 유통과학연구
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• 제20권11호
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• pp.121-129
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• 2022

Purpose: Artificial intelligence is widely used, particularly in the popular neural network theory called Deep learning. The improvement of computing speed and capability expedited the progress of Deep learning applications. The application of Deep learning in education has various effects and possibilities in creating and managing educational content and services that can replace human cognitive activity. Among Deep learning, Deep fake technology is used to combine and synchronize human faces with voices. This paper will show how to develop e-Learning content videos using those technologies and open-source tools. Research design, data, and methodology: This paper proposes 4 step development process, which is presented step by step on the Google Collab environment with source codes. This technology can produce various video styles. The advantage of this technology is that the characters of the video can be extended to any historical figures, celebrities, or even movie heroes producing immersive videos. Results: Prototypes for each case are also designed, developed, presented, and shared on YouTube for each specific case development. Conclusions: The method and process of creating e-learning video contents from the image, video, and audio files using Deep fake open-source technology was successfully implemented.

• Asia pacific journal of information systems
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• 제17권4호
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• pp.187-206
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• 2007

The development of computer and information technology has been combined with the information superhighway internet infrastructure, so information widely spreads not only in special fields but also in the daily lives of people. Information ubiquity influences the traditional way of transaction, and leads a new E-commerce which distinguishes from the existing E-commerce. Not only goods as physical but also service as non-physical come into E-commerce. As the scale of E-Commerce is being enlarged as well. It keeps people from finding information they want. Recommender systems are now becoming the main tools for E-Commerce to mitigate the information overload. Recommender systems can be defined as systems for suggesting some Items(goods or service) considering customers' interests or tastes. They are being used by E-commerce web sites to suggest products to their customers who want to find something for them and to provide them with information to help them decide which to purchase. There are several approaches of recommending goods to customer in recommender system but in this study, the main subject is focused on collaborative filtering technique. This study presents a possibility of pre-evaluation for the prediction performance of customer's preference in collaborative filtering before the process of customer's preference prediction. Pre-evaluation for the prediction performance of each customer having low performance is classified by using the statistical features of ratings rated by each customer is conducted before the prediction process. In this study, MovieLens 100K dataset is used to analyze the accuracy of classification. The classification criteria are set by using the training sets divided 80% from the 100K dataset. In the process of classification, the customers are divided into two groups, classified group and non classified group. To compare the prediction performance of classified group and non classified group, the prediction process runs the 20% test set through the Neighborhood Based Collaborative Filtering Algorithm and Correspondence Mean Algorithm. The prediction errors from those prediction algorithm are allocated to each customer and compared with each user's error. Research hypothesis : Two research hypotheses are formulated in this study to test the accuracy of the classification criterion as follows. Hypothesis 1: The estimation accuracy of groups classified according to the standard deviation of each user's ratings has significant difference. To test the Hypothesis 1, the standard deviation is calculated for each user in training set which is divided 80% from MovieLens 100K dataset. Four groups are classified according to the quartile of the each user's standard deviations. It is compared to test the estimation errors of each group which results from test set are significantly different. Hypothesis 2: The estimation accuracy of groups that are classified according to the distribution of each user's ratings have significant differences. To test the Hypothesis 2, the distributions of each user's ratings are compared with the distribution of ratings of all customers in training set which is divided 80% from MovieLens 100K dataset. It assumes that the customers whose ratings' distribution are different from that of all customers would have low performance, so six types of different distributions are set to be compared. The test groups are classified into fit group or non-fit group according to the each type of different distribution assumed. The degrees in accordance with each type of distribution and each customer's distributions are tested by the test of ${\chi}^2$ goodness-of-fit and classified two groups for testing the difference of the mean of errors. Also, the degree of goodness-of-fit with the distribution of each user's ratings and the average distribution of the ratings in the training set are closely related to the prediction errors from those prediction algorithms. Through this study, the customers who have lower performance of prediction than the rest in the system are classified by those two criteria, which are set by statistical features of customers ratings in the training set, before the prediction process.

• 한국콘텐츠학회논문지
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• 제18권10호
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• pp.78-98
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• 2018

2004년~2017년 8월까지 국내에 개봉된, 다양성 영화를 제외한 순수 국산 상업 극영화 855편만을 대상으로 하여 이들 영화의 감독, 제작자, 주연배우, 흥행성적을 조사하여 각각의 변수들에 대한 10분위 분석을 시행하였다. 다양성을 제외한 극영화 855편을 만드는 데에는, 감독은 509명, 제작자는 696명, 주연배우는 785여 명이 참여하였다. 프로야구 등 스포츠에는 많은 통계적 분석이 활용되고 있다. 승률, 점유율, 타율, 출루율, 도루성공률, 장타율, 삼진, 비율, 볼넷 비율, 홈런 비율 등이 스포츠 경기 결과를 예측하고, 프로선수들의 평가지표로 다뤄지고, 선수들의 연봉 협상의 참고자료가 되고 있다. 스포츠 경기처럼 우연이 많이 존재하는 영화 흥행에서도 영화의 퀄리티를 결정짓는, 창의력이 있어야 하는 인력들 즉 제작자, 감독, 주연배우 등의 평가에 10분위를 활용하여, 이들의 성과를 예측하거나 공헌도를 평가하는 데에 참고자료가 될 수는 없는지를 탐색하고자 한다. 본 연구에서는 대본 즉 시나리오에 대해서는 제작자, 배우, 감독이 선택하는 안목과 경륜을 통한 간접평가만을 담고 있다. 향후 시나리오 내러티브 분석의 정량화, 창작 인력의 성장과 쇠퇴를 볼 수 있는 시계열 분석, 창작 인력 간의 상호작용을 보는 네트워크 분석이 요구된다.

• 유통과학연구
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• 제15권10호
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• pp.41-50
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• 2017

Purpose - Online product review has become a crucial part of the online retailer's market performance for a wide range of products. This research aims to investigate how an individual reviewer's review frequency and timing affect her/his average attitude toward products. Research design, data, and methodology - To conduct reviewer-level analysis, this study uses 42,172 posted online review messages generated by 6,941 identified reviewers for 59 movies released in the South Korea from July 2015 to December 2015. This study adopts Tobit model specification to take into account the censored nature and the selection bias arising from the nature of J-shaped distribution of movie rating. Results - Our estimation results support that the negative impact of review frequency and timing on valence. Furthermore, review timing has an inverted-U relationship with the user's average valence and enhance the negative effect of review frequency. Conclusions - This study contributes to the growing literature on the understanding how eWOM is generated at the individual consumer level. On the basis of the main empirical findings, this study provides insights into building a recommendation system in online retail store based on the consumer's review history data - frequency, timing, and valence.

• 유통과학연구
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• 제15권4호
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• pp.33-40
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• 2017

Purpose - This study aims to examine the analysis of pattern on Arab countries consumers' preferences of the Korean Contents using social media, Facebook since Korean entertainment contents have been distributed in the global marketplace. Then we focus on developing Predictive model using a Data Mining Technique. Research design, data and methodology - In order to understand preference growth of Korean contents in Arabic countries, we- collected data from two popular Facebook pages: 'Korean movies and drama' and 'K-pop'. Then, we adopted a data-driven approach based on Data Mining techniques. Results - It is obvious that the number of likes for K-pop will increase for all North African and Middle Eastern countries, however concerning Korean Movies and Drama except Tunisia it is decreasing for Algeria, Egypt and Morocco. Also, concerning Saudi Arabia and United Arab Emirates, the number of likes will decrease for Korean Movies and Drama which is not the case for Iraq. Conclusions - It is noted in this study that K-contents such as drama, movie and music are sometimes a gateway to a wider interest in Korean culture, food and brands. Moreover, this study gives significant implications for developing predictive model to forecast Korean contents' consumption and preferences.

• 문화경제연구
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• 제21권1호
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• pp.3-20
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• 2018

본 연구는 한국 영화산업의 수직계열화 구조와 배급 및 상영시장의 집중도를 파악하고, 이 두 범주 간의 상호연관성에 대한 통합적인 맥락을 제시하고자 하였다. 한국영화 배급 및 상영시장의 집중성은 규모의 경제를 추구하고 수요의 불확실성을 방어하기 위한 산업 논리의 결과이다. 메이저 배급사는 대작영화에 자원을 집중하고 공급량을 조절함으로써 흥행위험을 방어하려는 경향이 강해지고 있으며, 최대한 많은 스크린을 확보하려는 배급 경쟁에 의해 멀티플렉스 체인의 협상력은 강화되고 있다. 수직결합 기업의 멀티플렉스는 계열관계에 따라 각 배급사의 영화에 차별적인 좌석수를 배정하는 것으로 나타났다. 그러나 수직계열화 기업마다 차별의 정도는 차이가 있으며, 상영 스크린수가 증가할수록 관객의 좌석점유율도 증가하고 있어서, 상영관이 계열관계에 따라 비합리적인 선택을 한다고 보기에는 무리가 있다. 따라서 수직결합구조가 스크린 독과점과 양극화의 원인이라는 일반화하기는 어려우며 유통시장의 집중성을 완화하고 중소영화를 지원하는 정책 방향이 요구된다.