• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.6
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• pp.2115-2130
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• 2022

With the development of over-the-top (OTT) services, the demand for content is increasing, and you can easily and conveniently acquire various content in the online environment. As a result, copyrighted content can be easily copied and distributed, resulting in serious copyright infringement. Some special forms of online service providers (OSP) use filtering-based technologies to protect copyrights, but illegal uploaders use methods that bypass traditional filters. Uploading with a title that bypasses the filter cannot use a similar search method to detect illegal content. In this paper, we propose a technique for profiling the Heavy Uploader by normalizing the bypassed content title and efficiently detecting illegal content. First, the word is extracted from the normalized title and converted into a bit-array to detect illegal works. This Bloom Filter method has a characteristic that there are false positives but no false negatives. The false positive rate has a trade-off relationship with processing performance. As the false positive rate increases, the processing performance increases, and when the false positive rate decreases, the processing performance increases. We increased the detection rate by directly comparing the word to the result of increasing the false positive rate of the Bloom Filter. The processing time was also as fast as when the false positive rate was increased. Afterwards, we create a function that includes information about overall piracy and identify clustering-based heavy uploaders. Analyze the behavior of heavy uploaders to find the first uploader and detect the source site.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.42 no.4
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• pp.173-182
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• 2019

This study attempts to improve the understanding of the rapidly growing online video platform market such as Youtube and OTT, and to investigate the attributes and relative importance of them. For this purpose, the factors that influence the choice to use were derived through literature studies and the Focus Group Interview (FGI), and the priority of the factors was calculated through the analytic hierarchy process (AHP). The upper layer of the AHP structure was 'Relationship', 'Entertainment', 'Informativity', and 'Convenience', and the lower layer was structured into 13 elements. The importance priority analysis among the factors that influence the choice to use was done by teenagers, 20s, and 30s and the results are summarized as follows : First, Users consider the 'Just for fun' and 'Satisfaction of interests' as the most important factors, followed by 'Easy accessibility to use', 'Vicarious satisfaction', 'Usefulness of Information', and 'Up-to-dateness of information'. Second, the ranking of the upper layer was in the order of 'Entertainment'-'Informativity'-'Convenience'-'Relationship'.As a result of AHP,'Entertainment' was 3.6 times more important than 'Relationship'. In the comparison by age group, only teenagers regarded that 'Convenience' is more important than 'Informativity'. According to the characteristics of the age group, the lower layer of teenagers consider 'Convenient function' to be important and ranked 'Usefulness of information' in 8^th. While 'Vicarious satisfaction' ranked 4th out of 13 factors in the entire age group, those in their 20s and 30s ranked 8^th, showing a difference. In the case of 20s, 'Reasonable price' was ranked 4th and the 'Diversity of Information' was ranked 5th, Otherwise 30s consider 'Trustworthiness of Information' to the third. Third, unlike 'Convenience' which was the lower-rank in the upper layer AHP analysis, 'Easy accessibility to use', the lower-layer of convenience, ranked third overall in the importance analysis among the 13 lower-layer factors, and showed a similar patterns in the age groups results. In the conclusion, this study demonstrates that 'Convenience' and 'Vicarious satisfaction' factors, which were not relatively well addressed in the previous studies, are the key factors to be considered in. By presenting the results of the importance analysis on each of the selected attributes, This study has a practical implication that Industries such as on-line video service platform provider can use the importance priority in establishing the directions of future strategy.

• The Journal of the Korea Contents Association
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• v.17 no.2
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• pp.521-534
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• 2017

In 2013 the Netflix, an OTT in USA, launched all at once 13 episodes of the House of Cards season. Binge-watching is the word which means watching continuously 2~6 episodes of a TV program with one sitting, the new normal of TV watching behavior, cultural and social currents all over the world. This study has analyzed the factors and motivations which affect to the persistent intention to use binge-watching. It conducted an online survey from 333 Quota sample from Korean age groups between 20th~60th with 81 questionnaires. The 5 groups were induced as motivation factors to binge-watching. The 3 groups which consisted of , , are affecting as positive to intention to use binge-watching. But the other 2 groups which are and doing as negative. The survey has shown that the persistent intention to binge-watching is affected by ages more younger, whom doing binge watching more frequently, whom estimating more higher to the conceived usefulness to use. As a theoretical model, expanded technology acceptance model was adopted and US drama House of Cards. This study could promote the next generation contents planning and S-VOD service industry.

• The Journal of the Korea Contents Association
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• v.20 no.5
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• pp.467-475
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• 2020

As the growth rate of IPTV VOD usage increases it is necessary to analyze VOD usage patterns systematically. This study divides VOD users into free VOD and paid VOD users, then explores how VOD usage motivations, usage patterns, and demographic factors affect the differences between two groups. The results show that social motivation, VOD satisfaction, using content after the holdback expiration, an intention to pay for ad-skip, the proportion of VOD usage, a VOD give-up experience, TV usage time, and SVOD usage time, are statistically significant. Except the VOD satisfaction factor, all of the factors analyzed are more likely to expect paid VOD users. Additionally this study found paid VOD users are more likely to use a SVOD service as an alternative one compared with free VOD users.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.2
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• pp.175-183
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• 2023

In a broadcasting market environment that has a close interdependence between platform operators and content operators, problems such as conflicts over program usage fees, and home shopping transmission fees are intensifying. This study attempted to analyze the environment of the domestic broadcasting market and present implications, analyze the cause of user fee conflict between the platform and PP, and propose detailed alternatives to resolve user fee conflict disputes. The results of environmental analysis on the domestic broadcasting market are as follows. First, the growth engine of the broadcasting industry has changed to direct resources such as service usage fees and content fees, and commerce is increasing. Second, as hegemony in the domestic broadcasting market changes from terrestrial to paid broadcasting and OTT, monopolies in the entire broadcasting area are being dismantled by voluntary entry. Third, the need to overhaul the existing regulatory system is increasing due to the dismantling and reorganization of the existing broadcasting market. On the other hand, this study proposed a strategy to diversify the profit structure of PP, supply program after pre-contracting, and strengthen CPS bargaining power in order to resolve disputes between paid broadcasting platforms and PP sharply. In particular, as strategies to strengthen CPS bargaining power of small and medium-sized SOs, it proposed to jointly improve CPS-related systems through IPTV and individual SOs, to redefine fees for programs and to voluntarily use programs.

• The Journal of the Convergence on Culture Technology
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• v.10 no.2
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• pp.499-503
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• 2024

Broadcasting programs are provided to various media such as Internet replay, OTT, and IPTV services as well as self-broadcasting. In this case, it is very important to provide keywords for search that represent the characteristics of the content well. Broadcasters mainly use the method of manually entering key keywords in the production process and the archive process. This method is insufficient in terms of quantity to secure core metadata, and also reveals limitations in recommending and using content in other media services. This study supports securing a large number of metadata by utilizing closed caption data pre-archived through the DTV closed captioning server developed in EBS. First, core metadata was automatically extracted by applying Google's natural language AI technology. The next step is to propose a method of finding core metadata by reflecting priorities and content characteristics as core research contents. As a technology to obtain differentiated metadata weights, the importance was classified by applying the TF-IDF calculation method. Successful weight data were obtained as a result of the experiment. The string metadata obtained by this study, when combined with future string similarity measurement studies, becomes the basis for securing sophisticated content recommendation metadata from content services provided to other media.

• Journal of Intelligence and Information Systems
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• v.29 no.3
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• pp.145-165
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• 2023

Recommendation system plays a significant role on relieving difficulties of selecting information among rapidly increasing amount of information caused by the development of the Internet and on efficiently displaying information that fits individual personal interest. In particular, without the help of recommendation system, E-commerce and OTT companies cannot overcome the long-tail phenomenon, a phenomenon in which only popular products are consumed, as the number of products and contents are rapidly increasing. Therefore, the research on recommendation systems is being actively conducted to overcome the phenomenon and to provide information or contents that are aligned with users' individual interests, in order to induce customers to consume various products or contents. Usually, collaborative filtering which utilizes users' historical behavioral data shows better performance than contents-based filtering which utilizes users' preferred contents. However, collaborative filtering can suffer from cold-start problem which occurs when there is lack of users' historical behavioral data. In this paper, hybrid music recommendation system, which can solve cold-start problem, is proposed based on the playlist data of Melon music streaming service that is given by Kakao Arena for music playlist continuation competition. The goal of this research is to use music tracks, that are included in the playlists, and metadata of music tracks and playlists in order to predict other music tracks when the half or whole of the tracks are masked. Therefore, two different recommendation procedures were conducted depending on the two different situations. When music tracks are included in the playlist, LightFM is used in order to utilize the music track list of the playlists and metadata of each music tracks. Then, the result of Item2Vec model, which uses vector embeddings of music tracks, tags and titles for recommendation, is combined with the result of LightFM model to create final recommendation list. When there are no music tracks available in the playlists but only playlists' tags and titles are available, recommendation was made by finding similar playlists based on playlists vectors which was made by the aggregation of FastText pre-trained embedding vectors of tags and titles of each playlists. As a result, not only cold-start problem can be resolved, but also achieved better performance than ALS, BPR and Item2Vec by using the metadata of both music tracks and playlists. In addition, it was found that the LightFM model, which uses only artist information as an item feature, shows the best performance compared to other LightFM models which use other item features of music tracks.