• Journal of Korean Library and Information Science Society
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• v.45 no.4
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• pp.309-336
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• 2014

The purpose of this study is to ascertain what kinds of books are selected for 'One Book, One City' campaign in Korea since 2003. For the purpose 473 selected books are analyzed. Based on the general overview of the campaign, selected books are analyzed by publication year, author, genre, and subject. From the analysis three preference tendencies in book selecting came out as newly published books, children's books, and regional characteristics reflected books.

• Science of Emotion and Sensibility
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• v.18 no.1
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• pp.59-66
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• 2015

To date, contents' usability of most multimedia devices has been focused on developer not on user, which made difficult in solving the problems or fulfilling the needs while people using real system. Although user-centered UX and UI researches have been studied and have resulted in innovation in some part, it does not show great effect on usability as it is not easy to interpret human emotions and needs and to apply those to system. Usability is the matter on how deeply smart devices can interpret and analyze human mind not on how much functions and technologies are improved. This study aims to help with usability improvement based on user when people use smart devices in multimedia environment. We studied the interaction between human and contents by analyzing the effect of human emotions and personalities on preference and consumption of contents' type. This study was done by assuming that proper analysis on human emotions may increase user satisfaction on multimedia environment. We analyzed contents preference by gender and emotion. The results showed that there is significant relationship between 'Happy' emotion and 'Comedy Program' preference and men are more prefer it than women. However, it does not reveal any significant relationship between 'Sad' emotion and contents preferences but women are slightly more prefer 'Comedy Program' than men. This result supports the Zillmann's 'mood based management', which suggests that the needs for pleasant contents are revealed to relieve sadness when people are in a sad mood. In addition, our finding corresponds with Oliver's insistence on meeting all four factors, insight, meaningfulness, understanding and reflection, rather than just pleasure for more satisfaction. This study focused on temporary emotional factors and contents and additionally on effect of users' emotion, personality and preference on type of contents consumption. This relationship between emotions and contents study would suggest the better direction for developing smart devices with great contents usability and user satisfaction in the future.

• 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 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.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2006.11a
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• pp.283-288
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• 2006

In the future television broadcasting a flood of information from various sources will not always be welcomed by everyone. The need of accessing specific information as required is becoming a necessity. We are interested to make the life of television consumer easier by providing an intelligent television set which can adaptively proposed certain shows to the viewer based on the user historical consumed shows. The TV watching history data consists of TV program titles with their respective genres, channels, watched times and durations, etc. The method proposed is by utilizing Hidden Markov Model (HMM) to model the user preference of kind of genres the viewer will watch based on recorded genres of several weeks time. We take watching schedule from 6 PM to midnight as boundary. The range thus divided into 3 independent time band of 2 hours each resulting in 3 time bands from 6 PM to 8 PM, 8 PM to 10 PM, and lastly 10 PM to midnight. Each time band will be represented by an HMM. From each HMM we can generate a sequence of predicted genre that the user will probably watch during corresponding time-band. Our approach assumes that the user shows a consistent behavior of watching pattern in week to week basis and during the moment of watching TV. To asses the method performance experiment is conducted using real data collected from December 2002 to May 2003. Some user's data are selected and based on that predictions are made. The resulting predictions are then compared with the actual user's history. The experiment shows satisfactory result for user with middle to high consistent behavior level.

• (The) Research of the performance art and culture
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• no.36
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• pp.531-551
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• 2018

The educational trait that the arts and cultural education and dance strive for at a time when the ethical tasks of life is the experience for insight of life. The awareness of time entrusted with the intensity [depth] of artistic and aesthetic experience is to contain its implication with policy and system. In the policy territory, broad perception and strategy are combined and practiced to produce new implication. Therefore, on the basis of characteristics and spectrum persuaded at a time when the arts and cultural education and dance education are broadly expanded, the result of this study after taking a look at the role of dance education within the arts and cultural education is shown as follows. The value striving for by the culture and arts education and dance education is to structure the life form with the artistic experience through the art as the ultimate life description. This is attributable to the fact that the artistic trait structured with self-understanding and self-expression contains the directivity of life that is recorded and depicted in the process of life. The dance education in the culture and arts education has the trait to view the world with the dance structure as the comprehensive study as in other textbook or art genre under the awareness of time and education system category within the school system and it has diverse social issues combined as related to the frame of social growth and advancement outside of school. When taking a look at the practical characteristics (method) of dance based on the arts and cultural education business, it facilitates the practice strategy through dance, in dance, about dance, between dance with the artist for art [dance]. At this time, the approachability of dance is deployed in a program based on diverse artistry for technology, expression, understanding, symbolism and others and it has the participation of enjoyment and preference. In the policy project of the culture and arts education, the dance education works as the function of education project as an alternative model on the education system and it also sometimes works as the function for social improvement and development to promote the community awareness and cultural transformation through the involvement and intervention of social issues.