• The Journal of the Korea Contents Association
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• v.15 no.4
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• pp.65-74
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• 2015

This study is dealing with sound of Hitchcock's movie <The Man Who Knew Too Much (1956)> from Peirce's semiotic perspective. This paper examined Peirce's theory prior to semiotic discussion, and analyzed the three elements of sound(speech, noise, music) depending on the type of sign that he presented. Music is possible to express emotions as an index through intervals. The instrument sound under firstness works as the element of narrative and it may be a dynamic object, transferred to the dicisign of secondness. Also, a word in speech is possible to represent an object allusively through an icon in the film. Noise and music as an index are serving to increase the tension of film. Meanwhile, two different genres of music on the juxtaposition are in charge of narrative function as a dicisign. Thus, Hitchcock's sound has various semiotic qualities of signification depending on the context.

• Journal of the Korea Society of Computer and Information
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• v.25 no.5
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• pp.99-106
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• 2020

The scalability problem inherent in collaborative filtering-based recommender systems has been an issue in related studies during past decades. Clustering is a well-known technique for handling this problem, but has not been actively studied due to its low performance. This paper adopts a clustering method to overcome the scalability problem, inherent drawback of collaborative filtering systems. Furthermore, in order to handle performance degradation caused by applying clustering into collaborative filtering, we take two strategies into account. First, we use fuzzy clustering and secondly, we propose and apply a similarity estimation method based on user preference for movie genres. The proposed method of this study is evaluated through experiments and compared with several previous relevant methods in terms of major performance metrics. Experimental results show that the proposed demonstrated superior performance in prediction and rank accuracies and comparable performance to the best method in our experiments in recommendation accuracy.

• Journal of Digital Contents Society
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• v.11 no.4
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• pp.607-614
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• 2010

History centered knowledge based story-telling project planning tool supports the process of story creation in narrative genre about history of families or individuals. Narrative fields not only include drama, mythology, legend, history but also non-verbal epics such as movie, play, ballet and opera. But as verbal epic, this research paper focuses on the family history and individual history of each household. This story-telling planning tool redevelops each genre of story-telling about family history through sampleDB and informationDB, and it is widely applicable in concreting high quality stories in both its content and value. Reduces the time of planning story-telling, and impose minimum expenses in human resources. Content about family history is one of the most the fundamental and renowned contents in Story-telling but planning tool that is easily applicable in creating such content does not exist in statue quo. In this current system lacking creative infra, this research paper seeks to provide a planning tool that public can easily utilize, and by systemizing the tool. it aims to create a creative contents tool model applicable in variety of genres.

• Journal of KIISE
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• v.42 no.5
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• pp.637-641
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• 2015

Due to the recent increasement of user created contents like SNS, blog posts, and so on, broadcast contents are actively re-construction by its users. Especially, on some genres like drama, movie, various information from cars and film sites to clothes and watches in a content is spreaded out to other users through blog postings. Since such information can be an additional information for the content, they can be used for providing high-quality broadcast services. For this purpose, in this paper, we propose timeline tag cloud generation method for broadcasting contents. In the proposed method, blog postings on the target contents are first gathered and then, images and words around images are extracted from a blog post as a tag set. An extracted tag set is tagged on a specific timeline of the target content. In experiments, to prove the efficiency of the proposed method, we evaluated the performances of the proposed image matching and tag cloud generation methods.

• Journal of Intelligence and Information Systems
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• v.19 no.1
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• pp.57-77
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• 2013

Social media has become one of the most popular media in web and mobile application. In 2011, social networks and blogs are still the top destination of online users, according to a study from Nielsen Company. In their studies, nearly 4 in 5active users visit social network and blog. Social Networks and Blogs sites rule Americans' Internet time, accounting to 23 percent of time spent online. Facebook is the main social network that the U.S internet users spend time more than the other social network services such as Yahoo, Google, AOL Media Network, Twitter, Linked In and so on. In recent trend, most of the companies promote their products in the Facebook by creating the "Facebook Page" that refers to specific product. The "Like" option allows user to subscribed and received updates their interested on from the page. The film makers which produce a lot of films around the world also take part to market and promote their films by exploiting the advantages of using the "Facebook Page". In addition, a great number of streaming service providers allows users to subscribe their service to watch and enjoy movies and TV program. They can instantly watch movies and TV program over the internet to PCs, Macs and TVs. Netflix alone as the world's leading subscription service have more than 30 million streaming members in the United States, Latin America, the United Kingdom and the Nordics. As the matter of facts, a million of movies and TV program with different of genres are offered to the subscriber. In contrast, users need spend a lot time to find the right movies which are related to their interest genre. Recent years there are many researchers who have been propose a method to improve prediction the rating or preference that would give the most related items such as books, music or movies to the garget user or the group of users that have the same interest in the particular items. One of the most popular methods to build recommendation system is traditional Collaborative Filtering (CF). The method compute the similarity of the target user and other users, which then are cluster in the same interest on items according which items that users have been rated. The method then predicts other items from the same group of users to recommend to a group of users. Moreover, There are many items that need to study for suggesting to users such as books, music, movies, news, videos and so on. However, in this paper we only focus on movie as item to recommend to users. In addition, there are many challenges for CF task. Firstly, the "sparsity problem"; it occurs when user information preference is not enough. The recommendation accuracies result is lower compared to the neighbor who composed with a large amount of ratings. The second problem is "cold-start problem"; it occurs whenever new users or items are added into the system, which each has norating or a few rating. For instance, no personalized predictions can be made for a new user without any ratings on the record. In this research we propose a clustering method according to the users' genre interest extracted from social network service (SNS) and user's movies rating information system to solve the "cold-start problem." Our proposed method will clusters the target user together with the other users by combining the user genre interest and the rating information. It is important to realize a huge amount of interesting and useful user's information from Facebook Graph, we can extract information from the "Facebook Page" which "Like" by them. Moreover, we use the Internet Movie Database(IMDb) as the main dataset. The IMDbis online databases that consist of a large amount of information related to movies, TV programs and including actors. This dataset not only used to provide movie information in our Movie Rating Systems, but also as resources to provide movie genre information which extracted from the "Facebook Page". Formerly, the user must login with their Facebook account to login to the Movie Rating System, at the same time our system will collect the genre interest from the "Facebook Page". We conduct many experiments with other methods to see how our method performs and we also compare to the other methods. First, we compared our proposed method in the case of the normal recommendation to see how our system improves the recommendation result. Then we experiment method in case of cold-start problem. Our experiment show that our method is outperform than the other methods. In these two cases of our experimentation, we see that our proposed method produces better result in case both cases.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.8
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• pp.4831-4843
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• 2014

Magazines on specific genres have been operating collaborative, co-working and collective production systems for value maximization using an adaptation strategy on the dynamic, complex and uncertain value network of the magazine industry. The study used a case study method, and data collection was performed by observational research, depth interviews and survey research. The subjects of the study were 'magazine industry', 'magazine firm and community', and 'collaboration system within creative users'. According to the research results, the ecosystem of magazines on a specific genre has been evolving into an innovative value network system, which is combined with the magazine firm, community users and magazine platform. Second, the rapid introduction of smart device environment changes the way of the collaborating system, in which an action and interaction came out within the community, creative users and magazine firms. Third, the production agency shows strong action and interaction, which fits the magazine platform within the ecosystem of a magazine on a specific genre well. This model has a similar fractal structure to the game, publishing, drama, movie, comic, and animation contents industry, converging to an innovative technology-based-creative-industry.

• Trans-
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• v.4
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• pp.87-109
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• 2018

The genres of fantastic of Korean films created in the 90s are diverse. The fantastics of these works are not manifested only in 'Ghosts' movies. For example, If the film Mystery Of The Cube, which is based on the mystery of Lee Sang, has a story of structuralist fantasy, we can say that a movie like Tell Me Something has a psychologically conventional fantastic. This study examines the fantastic of contemporary Korean films through 'completion of allegory based on realism'. For this, we borrows the fantastic concept of Etienne Souriau. In some films, fantastics can be found directly in the developing nature. In this case, the human being who justifies their domination of nature by upgrading their position as the objectifying subject of nature becomes 'alienated' in return. The notion of this alienation in film narratives is often revealed through allegories, particularly in the manifestation of fantasy by Étienne Souriau in the early 'personal experience'. Kim kiduk's film is a representative example of allegorizing elements of society through individual experience. Focusing on Kim Giduk's Bad Guy, this essay analyzes the process in which fantastic films of Korea in the 90s build up aesthetic fantastic through imitation of society.

• Korea and Global Affairs
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• v.3 no.1
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• pp.107-150
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• 2019

The article is intended to examine the four main vocalists, including Freddie Mercury, of the legendary rock group Queen, whose movie has generated a "Queen Syndrome" all over the world, and is widely shared by nearly 10 million viewers in Korea. In today's era of cultural content, this paper studied the relationship between their personality and character in the sense of storytelling for the four Queen members who left charismatic voices and beautiful pop music harmonies. As a theoretical background, I will focus our research on the members' psychological and human factors for the popularization of rock music, but I will apply the environmental factors that each member has been born and lived in, as well as the factors of family relations, such as parents. Queen is a band that has performed throughout the world for about 20 years, from the early 1970s to the birth and disappearance of a rock group. In sum, the four members of Queen, who have strong personalities, showed various musical possibilities by combining completely different genres such as acafella, ballad, opera and hard rock, depending on their taste and imagination, and achieved legendary fame that transcended generations by displaying excellent skills in communicating with the public. The four personality and characters, the rock group Queen and its main vocal Freddie Mercury, were the feet of association calculated by correlation from internal to external influences.

• Journal of Korea Entertainment Industry Association
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• v.13 no.6
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• pp.45-52
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• 2019

This study focuses on analyzing rhythms revealed in music songs of Korean third-generation idol girl groups. The development of genre and development of rhythm continues and the rhythm of Third World music is strong worldwide. The development of genres and rhythms continues and the rhythms of the Third World music are winning big popularity world wide. In the past, Third World rhythms were used only in countries where rhythm originated and neighboring countries, and the rhythms between countries were combined to occur in various conersence composite forms. In the past, the Third World rhythms were used only in rhythm-oriented countries and neighboring countries, and the rhythms between countries were combined in various and composite forms The changes in the global music market and the structure of the Korean pop music market show the same pattern in the local pop music genre. This study analyzes the phenomenon in which the Dembow rhythm and the tresillo rhythm, one of the rhythms of the Third World music, are concentrated in third-generation idol music, and notes that a certain number of combinations appear in rhythms and melodies. Moreover, this study identifies and compares the origin, the structure and the application cases of the Dembow rhythm and the tresillo rhythm by analyzing 12 songs of Korean third-generation idol girl groups. A game, as a sort of media like a movie, may be subjected to an ambivalent evaluation in accordance with the quality of each content. In this regard, it is necessary to have an active discussion about which game has desirable contents. Emotion which is an influential factor on interaction among players in game design needs to be approached from a social perspective. Also, based on social emotions associated with players, a significant relationship between emotional vocabularies and game emotions should be understood in comparison with social emotions within the game. based on social emotions regarding players As a result, social emotions within the game exhibited differences from the meaning of the terms. It is supposed that this result was caused by the ethical standards and differences in the real world. Accordingly, for qualitative enhancement of games, it is required to utilize the contents of this study, since emotions within the game are based on social emotions regarding players.

• Journal of Intelligence and Information Systems
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• v.16 no.3
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• pp.147-161
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• 2010

As broadcasting and communication are converged recently, communication is jointed to TV. TV viewing has brought about many changes. The IPTV (Internet Protocol Television) provides information service, movie contents, broadcast, etc. through internet with live programs + VOD (Video on demand) jointed. Using communication network, it becomes an issue of new business. In addition, new technical issues have been created by imaging technology for the service, networking technology without video cuts, security technologies to protect copyright, etc. Through this IPTV network, users can watch their desired programs when they want. However, IPTV has difficulties in search approach, menu approach, or finding programs. Menu approach spends a lot of time in approaching programs desired. Search approach can't be found when title, genre, name of actors, etc. are not known. In addition, inserting letters through remote control have problems. However, the bigger problem is that many times users are not usually ware of the services they use. Thus, to resolve difficulties when selecting VOD service in IPTV, a personalized service is recommended, which enhance users' satisfaction and use your time, efficiently. This paper provides appropriate programs which are fit to individuals not to save time in order to solve IPTV's shortcomings through filtering and recommendation-related system. The proposed recommendation system collects TV program information, the user's preferred program genres and detailed genre, channel, watching program, and information on viewing time based on individual records of watching IPTV. To look for these kinds of similarities, similarities can be compared by using ontology for TV programs. The reason to use these is because the distance of program can be measured by the similarity comparison. TV program ontology we are using is one extracted from TV-Anytime metadata which represents semantic nature. Also, ontology expresses the contents and features in figures. Through world net, vocabulary similarity is determined. All the words described on the programs are expanded into upper and lower classes for word similarity decision. The average of described key words was measured. The criterion of distance calculated ties similar programs through K-medoids dividing method. K-medoids dividing method is a dividing way to divide classified groups into ones with similar characteristics. This K-medoids method sets K-unit representative objects. Here, distance from representative object sets temporary distance and colonize it. Through algorithm, when the initial n-unit objects are tried to be divided into K-units. The optimal object must be found through repeated trials after selecting representative object temporarily. Through this course, similar programs must be colonized. Selecting programs through group analysis, weight should be given to the recommendation. The way to provide weight with recommendation is as the follows. When each group recommends programs, similar programs near representative objects will be recommended to users. The formula to calculate the distance is same as measure similar distance. It will be a basic figure which determines the rankings of recommended programs. Weight is used to calculate the number of watching lists. As the more programs are, the higher weight will be loaded. This is defined as cluster weight. Through this, sub-TV programs which are representative of the groups must be selected. The final TV programs ranks must be determined. However, the group-representative TV programs include errors. Therefore, weights must be added to TV program viewing preference. They must determine the finalranks.Based on this, our customers prefer proposed to recommend contents. So, based on the proposed method this paper suggested, experiment was carried out in controlled environment. Through experiment, the superiority of the proposed method is shown, compared to existing ways.