• Journal of Digital Convergence
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• v.19 no.7
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• pp.283-287
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• 2021

The purpose of this study is to analyze personalized contents and previews of OTT service based on user experience and to suggest the improvements of the preview viewing experience. Current domestic OTT mobile applications were researched to find out how services are offering content. Plus, the 20s-30s were recruited to analyze user experience. An online survey and in-depth interview were conducted by using Stephen P. Anderson's Creating Pleasurable Interface Model. As a result, preview help users to select content but it doesn't suit their taste. Also, the preview is hard to watch however they want. Therefore, it can be inferred that the preview requires the function for improving efficiency, preference, and accessibility. This study is expected to be used as research material on user experience or preview experience of OTT content.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.14 no.2
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• pp.115-121
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• 2021

This paper used big data and artificial intelligence technology to predict the rapidly increasing internet traffic. There have been various studies on traffic prediction in the past, but they have not been able to reflect the increasing factors that induce huge Internet traffic such as smartphones and streaming in recent years. In addition, event-like factors such as the release of large-capacity popular games or the provision of new contents by OTT (Over the Top) operators are more difficult to predict in advance. Due to these characteristics, it was impossible for an ISP (Internet Service Provider) to reflect real-time service quality management or traffic forecasts in the network business environment with the existing method. Therefore, in this study, in order to solve this problem, an Internet traffic collection system was constructed that searches, discriminates and collects traffic data in real time, separate from the existing NMS. Through this, the flexibility and elasticity to automatically register the data of the collection target are secured, and real-time network quality monitoring is possible. In addition, a large amount of traffic data collected from the system was analyzed by machine learning (AI) to predict future traffic of OTT operators. Through this, more scientific and systematic prediction was possible, and in addition, it was possible to optimize the interworking between ISP operators and to secure the quality of large-scale OTT services.

• International Journal of Contents
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• v.18 no.1
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• pp.65-75
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• 2022

This study aims to reveal the various differences within the factors that influence use satisfaction, continuous use intention, and media substitution intention, specifically in regard to the use motives and use behaviors of Netflix viewers. This study's results demonstrate that the following various factors affected use satisfaction, continuous use intention, and media substitution intention in differential ways. Firstly, the diversity of content influenced use satisfaction to a greater degree than social relations; and the diversity of content, social relations, and active participation all positively influenced continuous use intention. In other words, the more positively users appreciated the diversity of content, and the more strongly they had social relations, and the more actively they participated within communities, the greater degree to which they increased their continuous use intention for Netflix. However, the diversity of content and the convenience of use also had a negative effect on the media substitution intention for Netflix, which means that the more diverse the content and the more convenient the use of Netflix, the fewer the number of users who intended to cancel Netflix and subscribe to another OTT service or resubscribe to traditional media sources.

• Journal of Broadcast Engineering
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• v.24 no.6
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• pp.1013-1023
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• 2019

In content consumption environment with various live TV channels, VoD contents and web contents, recommendation service is now a necessity, not an option. Currently, various kinds of recommendation services are provided in the OTT service or the IPTV service, such as recommending popular contents or recommending related contents which similar to the content watched by the user. However, in the case of a content viewing environment through TV or IPTV which shares one TV and a TV set-top box, it is difficult to recommend proper content to a specific user because one or more usage histories are accumulated in one subscription information. To solve this problem, this paper interprets the concept of family as {user, time}, extends the existing recommendation relationship defined as {user, content} to {user, time, content} and proposes a method based on deep learning algorithm. Through the proposed method, we evaluate the recommendation performance qualitatively and quantitatively, and verify that our proposed model is improved in recommendation accuracy compared with the conventional method.

• The Journal of the Korea Contents Association
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• v.14 no.8
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• pp.342-364
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• 2014

The purpose of this paper is to provide an answer for the following question: What are the major shifts and implications of the unprecedently competitive and rapidly changing media ecosystem in the era of N-screen and OTT services? I've attempted to understand the complex and competitive nexus among media from an historical context by focusing on the displacement vs. complement thesis. The TPC model by Han has been employed for the analysis of the current dynamics of US media industries by triangulating three areas: Technology/industry, public policy and consumer/culture. More specifically, the US media landscape is initially divided into two competitive turfs - the competitors equipped with OTT services and the legacy media industry, and then the traditional media industry was grouped again into PayTV group(telecom service providers with IPTV and mobile TV, cable/Satellite TV networks) and Free (over-the-air) TV networks. Six major shifts were identified by the analysis: power shift in telecom carriers, power shifts in TV industry, Telecom/OTT partnership, time shifts, place shifts, and finally business model shifts.

• Journal of Broadcast Engineering
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• v.18 no.5
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• pp.749-757
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• 2013

This study aims to find out the influences of user behavior, content characteristics, functional factors and user's perception in cost on user satisfaction and continuous intention in N-Screen service. Web survey was conducted for 498 users who have used N screen service. The results show that the most influential factor contributing to user satisfaction is user interface. VOD diversity, payment system, cost, channel diversity are also meaningful factors. Identifying the critical factors which impact on user satisfaction, this study can provide the basic data for activating OTT service in smart media environment.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.26 no.6
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• pp.1505-1511
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• 2016

Live streaming is a method to provide media service by sending recoded media to a user's video player. In order to provide video and audio contents in real-time for a large number of users simultaneously, live streaming compatible protocols such as RTMP (Real Time Messaging Protocol), HLS (Http Live Streaming), are required. In this paper, we analyzed vulnerability of paid live streaming services with the captured packets from the applications used by six major OTT (over-the-top) companies in Korea supporting live streaming services. We found that streaming channels were not encrypted and access control mechanisms were not properly used. Thus, guest users can freely use paid live streaming services.

• The Journal of the Korea Contents Association
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• v.20 no.11
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• pp.38-48
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• 2020

The domestic AI speaker market is growing into a full-fledged early audience market beyond the innovative consumer market with 3 million domestic supply units at the end of 2018, but the reality is that for various reasons, we are not satisfied with the use. There are many previous papers on AI Speaker, but the majority of research so far tends to be biased towards the acceptance of the device's own performance. Many changes are being made, such as OTT providers trying to secure the market through collaboration with AI speaker providers. This study tried to identify the priorities for content services, which can be another major selection factor for AI speakers, excluding the factors of unsatisfactory technology. First, this study identified the priorities among AI speaker selection factors using AHP (Analytic Hierarchy Process), based on the AI speaker selection factors derived through literature research. The most important hierarchical factor are Concierge Service, Education Service, and Entertainment Service order in AI speaker selection, and the primary content among the individual factors was the one that ranked weather/temperature/fine dust (11.6%) and child caring content was in the second place (10.8%), and then music service was in the third place (9.8%). The three top priorities were derived from the items in the top tier 1, 2 and 3 priorities. Of the total 15 individual services, 6 sub-layers of Concierge Service (weather/temperature/fine dust, news, voice schedule notification) and Education Service (foreign language, toddler, reading books) were in the top 8, and two of the Entertainment Service Music service and movie service ranked third and sixth.

• Journal of Digital Contents Society
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• v.16 no.1
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• pp.137-144
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• 2015

This study tried to anticipate the near future changes of domestic online video, music service and media usage through the status quo. The research group was focused on the twenties, University students because they are the active media contents users. It surveyed the students in Busan, and used the method of descriptive statistics analysis for the understanding of the present state and near future prospect. This study shows that almost half of them use both services, and three people out of ten are the heavy users who use the services for over 3 hours a week. The streaming method is higher proportion than download method in using type. They are getting accustomed to pay contents cost, but it couldn't be said the paying content cost has been established. Preferred contents genre appears to be the RMC(Ready Made Contents) such as existing broadcaster contents and movies. Notable result on media usage was the proportion of the so-called 'Zero-TV'. It was 32%, significantly higher than the proportion of the total population(4.4%). Integrating the analysis results, we can expect that the usage pattern will be changed gradually, thus the advent of various revenue models will emerge.

• Journal of Digital Contents Society
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• v.15 no.3
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• pp.327-338
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• 2014

This study proposes the evolution model of emerging media platform including smart media (M-IPTV, Smart TV) and IPTV, through a spiral evolution model based on the theory of the exploration & the exploitation. Authors suppose that the IP media evolved from IPTV technology, evolution of which has come through the development model of serial circular process. For explanation of this evolution, we propose the research model integrating the spiral model and the exploration & exploitation theory. Researchers define the smart media as the evolved media from IPTV, through this proposed model. We expect this model to be the theoretical base of media regulation and setting direction of future media service. Thus, this research are summed up as follows. To begin with, we classify the evolutionary stage of IPTV from the view points of service feature, regulation frame and technological characters. Secondly, we organize and confirm the research model of IPTV evolution through case analysis of the 3 cycles of each evolutionary stage. As a result, discussion of this study helps understanding mobile IP media and smart media as a post IPTV and leads the solution of various regulation issues and the development of the innovative media service and content. In addition, it can guide the government for the evolutionary direction to promote the media and the content industry.