• 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 Internet Computing and Services
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• v.17 no.3
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• pp.107-113
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• 2016

Following the recent advancement in the use of social networks, a vast amount of different online reviews is created. These variable online reviews which provide feedback data of contents' are being used as sources of valuable information to both contents' users and providers. With the increasing importance of online reviews, studies on opinion mining which analyzes online reviews to extract opinions or evaluations, attitudes and emotions of the writer have been on the increase. However, previous sentiment analysis techniques of opinion-mining focus only on the classification of reviews into positive or negative classes but does not include detailed information analysis of the user's satisfaction or sentiment grounds. Also, previous designs of the sentiment analysis technique only applied to one content domain that is, either product or movie, and could not be applied to other contents from a different domain. This paper suggests a sentiment analysis technique that can analyze detailed satisfaction of online reviews and extract detailed information of the satisfaction level. The proposed technique can analyze not only one domain of contents but also a variety of contents that are not from the same domain. In addition, we design a system based on Hadoop to process vast amounts of data quickly and efficiently. Through our proposed system, both users and contents' providers will be able to receive feedback information more clearly and in detail. Consequently, potential users who will use the content can make effective decisions and contents' providers can quickly apply the users' responses when developing marketing strategy as opposed to the old methods of using surveys. Moreover, the system is expected to be used practically in various fields that require user comments.

• Journal of Intelligence and Information Systems
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• v.17 no.1
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• pp.139-152
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• 2011

Recommender systems have become an important research field since the emergence of the first paper on collaborative filtering in the mid-1990s. In general, recommender systems are defined as the supporting systems which help users to find information, products, or services (such as books, movies, music, digital products, web sites, and TV programs) by aggregating and analyzing suggestions from other users, which mean reviews from various authorities, and user attributes. However, as academic researches on recommender systems have increased significantly over the last ten years, more researches are required to be applicable in the real world situation. Because research field on recommender systems is still wide and less mature than other research fields. Accordingly, the existing articles on recommender systems need to be reviewed toward the next generation of recommender systems. However, it would be not easy to confine the recommender system researches to specific disciplines, considering the nature of the recommender system researches. So, we reviewed all articles on recommender systems from 37 journals which were published from 2001 to 2010. The 37 journals are selected from top 125 journals of the MIS Journal Rankings. Also, the literature search was based on the descriptors "Recommender system", "Recommendation system", "Personalization system", "Collaborative filtering" and "Contents filtering". The full text of each article was reviewed to eliminate the article that was not actually related to recommender systems. Many of articles were excluded because the articles such as Conference papers, master's and doctoral dissertations, textbook, unpublished working papers, non-English publication papers and news were unfit for our research. We classified articles by year of publication, journals, recommendation fields, and data mining techniques. The recommendation fields and data mining techniques of 187 articles are reviewed and classified into eight recommendation fields (book, document, image, movie, music, shopping, TV program, and others) and eight data mining techniques (association rule, clustering, decision tree, k-nearest neighbor, link analysis, neural network, regression, and other heuristic methods). The results represented in this paper have several significant implications. First, based on previous publication rates, the interest in the recommender system related research will grow significantly in the future. Second, 49 articles are related to movie recommendation whereas image and TV program recommendation are identified in only 6 articles. This result has been caused by the easy use of MovieLens data set. So, it is necessary to prepare data set of other fields. Third, recently social network analysis has been used in the various applications. However studies on recommender systems using social network analysis are deficient. Henceforth, we expect that new recommendation approaches using social network analysis will be developed in the recommender systems. So, it will be an interesting and further research area to evaluate the recommendation system researches using social method analysis. This result provides trend of recommender system researches by examining the published literature, and provides practitioners and researchers with insight and future direction on recommender systems. We hope that this research helps anyone who is interested in recommender systems research to gain insight for future research.

• International Commerce and Information Review
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• v.17 no.3
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• pp.73-91
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• 2015

This study investigated how Korean image and cosmetics products image effect by the Korean Wave(Hanllyu) on the Vietnam Customer focus on the cosmetics. Especially, Korean Wave of this study consisted in the movie/drama, K-pop and Korean stars. Korean image and cosmetics brand image was effected by factors related to the Korean Wave which had been deducted, based on preceeding research. A study was conduct in Vietnam where the spread of the Korean Wave content customer bases has been over along time. A total of 295 samples were used for th final analysis. Data analysis consisted of descriptive statistics, Cronbach's alpha, a confirmatory factor analysis, and multi-regression. It is also found that Korean image and cosmetics brand image has affect by Korean Wave. and Cosmetics purchasing intention has affect Korean image and cosmetics brand image.

• Journal of Information Technology Applications and Management
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• v.21 no.4
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• pp.203-223
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• 2014

This study proposes a novel context-aware recommender system, which is designed to recommend the items according to the customer's responses to the previously recommended item. In specific, our proposed system predicts the user's emotional state from his or her responses (such as facial expressions and movements) to the previous recommended item, and then it recommends the items that are similar to the previous one when his or her emotional state is estimated as positive. If the customer's emotional state on the previously recommended item is regarded as negative, the system recommends the items that have characteristics opposite to the previous item. Our proposed system consists of two sub modules-(1) emotion prediction module, and (2) responsive recommendation module. Emotion prediction module contains the emotion prediction model that predicts a customer's arousal level-a physiological and psychological state of being awake or reactive to stimuli-using the customer's reaction data including facial expressions and body movements, which can be measured using Microsoft's Kinect Sensor. Responsive recommendation module generates a recommendation list by using the results from the first module-emotion prediction module. If a customer shows a high level of arousal on the previously recommended item, the module recommends the items that are most similar to the previous item. Otherwise, it recommends the items that are most dissimilar to the previous one. In order to validate the performance and usefulness of the proposed recommender system, we conducted empirical validation. In total, 30 undergraduate students participated in the experiment. We used 100 trailers of Korean movies that had been released from 2009 to 2012 as the items for recommendation. For the experiment, we manually constructed Korean movie trailer DB which contains the fields such as release date, genre, director, writer, and actors. In order to check if the recommendation using customers' responses outperforms the recommendation using their demographic information, we compared them. The performance of the recommendation was measured using two metrics-satisfaction and arousal levels. Experimental results showed that the recommendation using customers' responses (i.e. our proposed system) outperformed the recommendation using their demographic information with statistical significance.

• Journal of Advanced Navigation Technology
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• v.18 no.5
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• pp.409-414
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• 2014

Since the five-day workweek has implemented and the spare time increased, the tourist industry has been showing the growth with quality. The tourist industry takes center stage as the twenty-first century higher value-added business on the strength of electronic communication development. Especially as being surrounded by water on three sides and national income has incremented, people have the interest in marine leisure industry. The Ministry of Maritime Affairs and Fisheries carries out a plan to promote marine tourism promotion plan through the significant policy support. Also, they makes an effort to lure the tourist through blending cultures. Therefore, through the 360 degree camera, the activity that mobilizes the policy fund can be monitored rightly and the application strategy that is useful to promote a higher value-added tourist industry can be suggested.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.4
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• pp.755-762
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• 2016

Motion graphic is the recently emerged technique which extends the ways of expression in video industry. Currently, it is worldwide trends that the image design gets more attention in the field of movie, advertisement, exhibition, web, mobile, games and new media, etc. With the development of computer's new technologies, VFX methods for the visual content is dynamically changed. Such production methods combine the real scenary and CG(Computer Graphic) to compose realistic scenes, which cannot be pictured in the ways of ordinary filming. This methods overcome the difference between the real and virtual world, maximize the expressive ways in graphics and real space. Match moving is technique of accurate matching between real and virtual camera to provide realistic scene. In this paper we propose the novel technique for motion graphic image production. In this framework we utilize the match moving methods to get the movements of the real camera into 3D layer data.

• Knowledge Management Research
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• v.17 no.3
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• pp.181-206
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• 2016

Under the influence of growing popularity of "hallyu" (Korean wave), corporates that have copyrights such as music, movie, drama as their core competitiveness are showing continuing growth. In Addition, they built on contents are rapidly growing, interests in protection and management of intellectual property rights linked to contents are growing. Global contents development corporates are making great efforts to create profits out of copyrights. They could utilize original contents to strengthen brand value use it to produce additional contents in current market. Also they take advantage of existing storyline of the contents and strong brand to explore new markets. This paper looks into Value articulation model by Professor James Conley and analyzed the firms that utilized intellectual property rights to extend the period of protection, strengthen their competitiveness and succeeded in breaking into new market by using the rights they possess. Also, this paper examines the usage of intellectual property rights and business expansion strategy of of Iconix, the Korean entertainment company, which gained tremendous popularity in last ten years using this model. In Value articulation model, Conley classifies the process of exploiting the portfolio of the single product's(or service's) intellectual property right for a period of time into three stages ; value transference, value translation, value transportation. Pororo's strategy of utilizing intellectual property right is suggestive to domestic entertainment companies. Under the influence of hallyu" (Korean wave), domestic contents such as movies, dramas and music are enjoying the high level of popularity recently not to mention animations. In reality, Korean entertainment companies who have no background or experience of Intellectual property rights are not creating enough added values compared to fast growing market. It is believed Iconix's intellectual property rights management strategy will suggest positive aspects to domestic companies. Moreover, I hope various intellectual property rights management strategies including Conley's value articulation are studied and they can make contributions to managing domestic entertainment companies.

• Journal of Digital Contents Society
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• v.3 no.1
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• pp.59-74
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• 2002

Due to rapid advance of wireless communication technology and popularization of wireless devices, demand on wireless internet contents is gradually increasing. Therefore, there are many researches and solution developments to provide good qualified contents quickly for wireless internet. For example, researches into converting wired web contents into wireless web contents or using integrated markup language like XHTML basic to make contents. In this paper, I propose a method to develop XML-based web contents which uses PHP language for data fetch from MySQL database. This method use open source software for a cost saving. Due to use of PHP extension as a XSLT engine, this method is very easy to apply. For a example of this method, a web content of movie information is implemented for PC and PDA browser. Developing XML-based web contents is useful not only for supporting devices of multiple type, but also for rapid changes of user interface design and for exchange of contents between web sites.

• Journal of Broadcast Engineering
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• v.20 no.3
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• pp.430-439
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• 2015

The demand of violence detection techniques using a video analysis to help prevent crimes is increasing recently. Many researchers have studied vision based behavior recognition but, violent behavior analysis techniques usually focus on violent scenes in television and movie content. Many methods previously published usually used both a color(e.g., skin and blood) and motion information for detecting violent scenes because violences usually involve blood scenes in movies. However, color information (e.g., blood scenes) may not be useful cues for violence detection in surveillance videos, because they are rarely taken in real world situations. In this paper, we propose a method of violent behavior detection in surveillance videos using motion vectors such as flow vector magnitudes and changes in direction except the color information. In order to evaluate the proposed algorithm, we test both USI dataset and various real world surveillance videos from YouTube.