• The Journal of the Korea Contents Association
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• v.22 no.7
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• pp.164-175
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• 2022

The OTT (Over The Top) platform promotes itself as a distinctive competitive advantage in that it allows users to stay on the platform longer and visit more often through a Personalized Recommendation Service. In this study, the characteristics of the Personalized Recommendation Service are divided into three categories: recommendation accuracy, recommendation diversity, and recommendation novelty. Then proposed a research model which affects the usefulness of users to recognize recommendation services by each characteristics and leads to satisfaction of expectations. The result of conducting an online survey of 300 people in their 20s and 30s who subscribe Netflix shows that the perceived usefulness increased when the accuracy, variety, and novelty of Netflix's Recommendation Service were high. It was also confirmed that high perceived usefulness leads to satisfaction of expectations before and after Netflix use. The derived research results can confirm the importance of evaluating the personalized recommendation service in terms of user experience and provide implications for ways to improve the quality of recommendation services.

• Journal of Korea Society of Digital Industry and Information Management
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• v.18 no.1
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• pp.55-64
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• 2022

This study analyzes the success factors of OTT original content through big data, and intends to suggest scenarios, casting, fun, and moving elements when producing the next work. In addition, I would like to offer suggestions for the success of 'Squid Game Season 2'. The success factor of 'Squid Game' through big data is first, it is a simple psychological experimental game. Second, it is a retro strategy. Third, modern visual beauty and color. Fourth, it is simple aesthetics. Fifth, it is the platform of OTT Netflix. Sixth, Netflix's video recommendation algorithm. Seventh, it induced Binge-Watch. Lastly, it can be said that the consensus was high as it was related to the time to think about 'death' and 'money' in a pandemic situation. The suggestions for 'Squid Game Season 2' are as follows. First, it is a fusion of famous traditional games of each country. Second, it is an AI-based planned MD product production and sales strategy. Third, it is casting based on artificial intelligence big data. Fourth, secondary copyright and copyright sales strategy. The limitations of this study were analyzed only through external data. Data inside the Netflix platform was not utilized. In this study, if AI big data is used not only in the OTT field but also in entertainment and film companies, it will be possible to discover better business models and generate stable profits.

• The Journal of the Korea Contents Association
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• v.14 no.5
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• pp.30-43
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• 2014

The number of online video content distributors are rapidly increasing in N-screen environment. The purpose of this study is to analyze the business model of these distributors, especially Netflix and Hulu. Basis on STOF model, environmental domain, service domain, organization domain and financial domain of each company are analyzed and compared. This study finds that each company's business model had distinct characters in earlier stage. Netflix offered online and offline service in low cost, while Hulu offered up-to-date TV contents without fees. However, two companies' business models are getting similar. The result of this study shows that highly competitive environment and increasing content budget affect two companies to adopt similar business model to survive.

• Journal of Digital Convergence
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• v.17 no.2
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• pp.399-404
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• 2019

This study proposes a strategy for developing Korean entertainment video service by studying the current status of user's experience of OTT(Over the Top, online video streaming service), mainly Netflix and Watcha Play. Firstly, as case study research, I investigated the features of domestic video streaming services and that of foreign services and K-content service needs. Secondly, I interviewed eight Netflix and Watcha Play users to understand the user experience and the demand for K-content video streaming service. As a result, I was able to derive two points about the strategy. First, isolated channel strategy. Second, content diversification and personalization strategy. This study is meaningful that it presented a strategy for the direction of the Korean entertainment industry. I hope that the follow-up study will help improve the Korean entertainment industry and help develop Korea's entertainment strategy.

• Journal of Korea Society of Industrial Information Systems
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• v.27 no.4
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• pp.47-64
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• 2022

This study attempted to identify the effect of personal innovation, perceived ease of use and perceived usefulness on the intention to use for users of OTT service platform Netflix, and finally affects flow and addiction. According to the research results, personal innovation has a positive impact on compatibility, simplicity and perceived usefulness, simplicity and perceived usefulness have a positive effect on use intention, compatibility and use intention have a positive effect on flow, and flow has a positive effect on addiction. This study is meaningful in that it can improve the understanding of user intention based on Netflix's new subscription standard and provide basic data that can be used for basic research on OTT service users' flow and addiction and development strategies of OTT content providers.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.281-283
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• 2021

The representative companies mentioned in the recommendation service in the domestic OTT(Over-the-top media service) market are YouTube and Netflix. YouTube, through various methods, started personalized recommendations in earnest by introducing an algorithm to machine learning that records and uses users' viewing time from 2016. Netflix categorizes users by collecting information such as the user's selected video, viewing time zone, and video viewing device, and groups people with similar viewing patterns into the same group. It records and uses the information collected from the user and the tag information attached to the video. In this paper, we propose a method to improve video media recommendation by automatically generating metadata of video media that was written by hand.

• Journal of Information Processing Systems
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• v.18 no.6
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• pp.729-740
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• 2022

Netflix, Amazon Prime, and YouTube are the most popular and fastest-growing streaming services globally. It is a matter of great interest for the streaming service providers to preview their service infrastructure and streaming strategy in order to provide new streaming services. Hence, the first part of the paper presents a detailed survey of the Content Distribution Network (CDN) and cloud infrastructure of these service providers. To understand the streaming strategy of these service providers, the second part of the paper deduces a common quality-of-service (QoS) model based on rebuffering time, bitrate, progressive download ratio, and standard deviation of the On-Off cycle. This model is then used to analyze and compare the streaming behaviors of these services. This study concluded that the streaming behaviors of all these services are similar as they all use Dynamic Adaptive Streaming over HTTP (DASH) on top of TCP. However, the amount of data that they download in the buffering state and steady-state vary, resulting in different progressive download ratios, rebuffering levels, and bitrates. The characteristics of their On-Off cycle are also different resulting in different QoS. Hence a thorough adaptive bit rate (ABR) analysis is presented in this paper. The streaming behaviors of these services are tested on different access network bandwidths, ranging from 75 kbps to 30 Mbps. The survey results indicate that Netflix QoS and streaming behavior are significantly consistent followed by Amazon Prime and YouTube. Our approach can be used to compare and contrast the streaming services' strategies and finetune their ABR and flow control mechanisms.

• Journal of Intelligence and Information Systems
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• v.18 no.2
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• pp.131-141
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• 2012

In this paper, we propose a new method for collaborative filtering (CF)-based recommender systems. Traditional CF-based recommendation algorithms have applied constant settings such as a reference group (neighborhood) size and a significance level to all users. In this paper we develop a new method that identifies optimal personalized settings for each user and applies them to generating recommendations for individual users. Personalized parameters are identified through iterative simulations with 'training' and 'verification' datasets. The method is compared with traditional 'constant settings' methods using Netflix data. The results show that the new method outperforms traditional, ordinary CF. Implications and future research directions are also discussed.

• The Journal of Information Systems
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• v.29 no.4
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• pp.193-208
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• 2020

Purpose Due to COVID19, the over-the-top media service (OTT) market is growing faster than expected at an annual average of 26.4%. In Korea, WAVVE, which integrated SKT's Oksusu and POOQ in September 2019, outperformed Netflix in the number of users immediately after its launch, but the number of users gradually decreased. Research on OTT investigated the spread of new media due to changes in regulations or policy, mostly in broadcasting media. On the other hand, OTT research in information systems began after the success of Netflix. It investigated consumers' satisfaction with information technology using the Information Technology Acceptance Model (TAM). This study investigates changes in consumer perceptions in the OTT market, which has grown after the Netflix's entry into Korea, the emergence of WAVVE and new OTT service providers, and the spread of COVID19. Design/methodology/approach This study selects contents, fees, service quality, and additional services as factors to evaluate consumer perception using AHP. Findings According to the 101 respondents, the content was the most important factor, followed by service quality, fees, and additional services. Contrary to previous findings that price is the determining factor in service adoption, this study reveals that consumers are willing to pay a reasonable amount for rich content and excellent service quality. Future research will use demographic analysis to reveal differences in consumer's perceptions of service selection.

• The Journal of the Korea Contents Association
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• v.19 no.7
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• pp.384-392
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• 2019

Big data-related research that deals with the amount of explosive information in the era of the Fourth Industrial Revolution is actively underway. Big data is an essential element that promotes the development of artificial intelligence with a wide range of data that become learning data for machine learning, or deep learning. The use of deep learning and big data in various fields has produced meaningful results. In this paper, we have investigated the use of Big Data in the cultural arts industry, focusing on video contents. Noteworthy is that big data is used not only in the distribution of cultural and artistic contents but also in the production stage. In particular, we first looked at what kind of achievements and changes the Netflix in the US brought to the OTT business, and analyzed the current state of the OTT business in Korea. After that, Netflix analyzed the success stories of 'House of Cards', which was produced / circulated through 'Deep Learning' cinematique, which is a prediction algorithm, through accumulated customer data. After that, FGI (Focus Group Interview) was held for cultural and artistic contents experts. In this way, the future prospects of Big Data in the domestic culture and arts industry are divided into technical aspect, creative aspect, and ethical aspect.