• Journal of Digital Contents Society
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• v.18 no.4
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• pp.707-712
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• 2017

As the amount of data increases exponentially, the recommender system is attracting interest in various industries such as movies, books, and music, and is being studied. The recommendation system aims to propose an appropriate item to the user based on the user's past preference and click stream. Typical examples include Netflix's movie recommendation system and Amazon's book recommendation system. Previous studies can be categorized into three types: collaborative filtering, content-based recommendation, and hybrid recommendation. However, existing recommendation systems have disadvantages such as sparsity, cold start, and scalability problems. To improve these shortcomings and to develop a more accurate recommendation system, we have designed a recommendation system as a factorization machine using actual online product purchase data.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.17 no.2
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• pp.45-50
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• 2017

Micro-service Architecture is a service architecture optimized for large-scale distributed systems such as real-time realistic broadcasting systems, so that are fiercely adopted by Global leading service platform vendors such as Netflix and Twitter due to the merit of horizontal performance scalability enabling the scale-out technique. In addition, micro-service architecture makes it possible to execute image processing and real-time data analysis using an asynchronous-based processing that are difficult to handle in Web API such as REST. In this paper, an event stream processing framework applicable to asynchronous based micro services is proposed in the sense that the accountability of event processing order is not guaranteed in the events such as IoT sensor data analysis or cloud-based image editing because these are the situations where the real-time media editing generates multiple event streams and asynchronous processes in the platform.

• The Journal of Information Systems
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• v.29 no.2
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• pp.221-242
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• 2020

Purpose This study collects data of the recently activated online black market and analyzes it to present a specific method for preparing for a hacking attack. This study aims to make safe from the cyber attacks, including hacking, from the perspective of individuals and businesses by closely analyzing hacking methods and tools in a situation where they are easily shared. Design/methodology/approach To prepare for the hacking attack through the online black market, this study uses the routine activity theory to identify the opportunity factors of the hacking attack. Based on this, text mining and social network techniques are applied to reveal the most dangerous areas of security. It finds out suitable targets in routine activity theory through text mining techniques and motivated offenders through social network analysis. Lastly, the absence of guardians and the parts required by guardians are extracted using both analysis techniques simultaneously. Findings As a result of text mining, there was a large supply of hacking gift cards, and the demand to attack sites such as Amazon and Netflix was very high. In addition, interest in accounts and combos was in high demand and supply. As a result of social network analysis, users who actively share hacking information and tools can be identified. When these two analyzes were synthesized, it was found that specialized managers are required in the areas of proxy, maker and many managers are required for the buyer network, and skilled managers are required for the seller network.

• Industry Promotion Research
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• v.6 no.4
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• pp.11-21
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• 2021

This study tried to analyze the factors of corporate growth potential of CJ E&M, a representative company in the content industry. First, in order to analyze CJ E&M's growth Engine, Michael Porter's diamond model was used to review key factors, and then, based on the 4P model, directions for future corporate growth were suggested. As a Result, Factors behind corporate growth included "Media content of various genres" and "recruitment of star-class human resources", in terms of Production Conditions, and "Gratification Chinese market demand" in terms of Demand Conditions. In addition, "Korean wave industry aiming at K-Culture" in terms of Related Industry and "Two track strategy: global·glocal strategy" and "Media commerce strategy" in terms of business environment: strategy, structure, and competition was able to analyze. For the direction of development, there are "various products through collaboration of affiliated companies" in terms of Product and "TVING Benchmarked Netflix" in terms of Price. In addition, "global expansion through OTT platform TVING" in terms of place and "challenge marketing utilizing the Tiktok platform" in terms of promotion must be carried out.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.3
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• pp.67-72
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• 2021

Association rule mining is a method of showing the relationship between patterns hidden in several tables. These days, granulation logic is used to add more detailed meaning to association rule mining. In addition, unlike the existing system that recommends using existing data, the granulation related rules can also recommend new subscribers or new products. Therefore, determining the qualitative size of the granulation of the association rule determines the performance of the recommendation system. In this paper, we propose a granulation method for subscribers and movie data using fuzzy logic and Shannon entropy concepts in order to understand the relationship to the movie evaluated by the viewers. The research is composed of two stages: 1) Identifying the size of granulation of data, which plays a decisive role in the implications of the association rules between viewers and movies; 2) Mining the association rules between viewers and movies using these granulations. We preprocessed Netflix's MovieLens data. The results of meanings of association rules and accuracy of recommendation are suggested with managerial implications in conclusion section.

• The Journal of the Korea Contents Association
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• v.21 no.6
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• pp.494-504
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• 2021

The spread of Over-The-Top (OTT) service, which represents Netflix, and the social distancing caused by COVID-19, acted as an overall bad news for domestic multiplex movie theaters. In addition to this, the phenomenon of digital shifting was added, and the need for domestic offline movie theaters to seek a new market for growth emerged. This study focused on the concept of consumer value perception amid this problem consciousness, and attempted to investigate the relationship between the brand image of multiplex movie theaters and purchase satisfaction according to the type of consumer value perception. After data was sampled through a questionnaire survey to a total of 350 subjects, the results of empirical analysis according to the study model are as follows. Among the types of value perception of offline movie viewers, practicality had the strongest influence on brand image construction, and self-faithfulness had the strongest influence on purchase satisfaction of offline movie watching. In addition, the brand image of offline movie theaters had a positive(+) effect on the purchase satisfaction of moviegoers. Based on this, this study suggested a new survival strategy in the new normal era of offline Multiplex Cinemas.

• 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.

• The Journal of the Korea Contents Association
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• v.21 no.8
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• pp.60-68
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• 2021

The purpose of this paper is to examine the formal characteristics of , which has been screened on Netflix since 2018. This film can be considered an interactive narrative because it gives viewers the opportunity to select their own narrative forks which lead to various endings. However, it also limits viewers' freedom of interactions in many ways, resulting in the pessimistic narrative world of series. In this contradictory situation, the conflict between the user's selectability and the narrator's authoriality emerges. And this collision gives rise to a complex form in which nonlinear interactive and linear narrative forms blend together. It can be understood as a form of self-reflection, such as forms of the metalepsis and breaking the fourth wall. In this paper, this particular form will be regarded as a sort of reflexive intermediality, i. e. the form for media reflexion.

• Journal of The Korean Association of Information Education
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• v.25 no.6
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• pp.927-934
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• 2021

AI(Artificial intelligence) is gradually taking up a large part of our lives, and the pace of AI development is accelerating. It is called ACT that develop students' computational thinking in the way artificial intelligence learns. Among ACTs, pattern recognition is an essential factor in efficiently solving problems. Pattern analysis is part of the pattern recognition process. In fact, Netflix's personalized movie recommendation service and what it named Covid-19 after repeated symptoms are all the results of pattern analysis. While the importance of ACT, including pattern recognition, is highlighted, software education for kindergarten and elementary school lower grades is much insufficient compared to foreign countries. Therefore, this study aims to design and develop textbooks for the development of artificial intelligence-based computational thinking through pattern analysis for kindergarten students.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.3
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• pp.223-228
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• 2023

According to the Korea Information Society Development Institute, OTT services grew at a rate of 33.4% in four yearsfrom 2017, when they were first launched.TheKorea Export-Import Bank announced in 2020 that the domestic OTT market was worth 780.1 billionKRW. This growth of the OTT market is expected to stimulate competition among OTT service platforms, and user satisfactionwithconvenience features, such as video recommendations, seems to be acting as an important factor in the competition.Currently, the OTT market uses a variety ofdata for customized recommendations, but the limitationis that it only uses datacollected within the app. Thereby we have proposed the use ofpersonal data collected outside the app for personalized recommendations, and the survey results showed that user satisfaction was 23.72% higher for recommended content based on the proposedmethod thanNetflix recommended content.