• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.25 no.4
• /
• pp.537-542
• /
• 2021

Smart TVs provide a variety of services and information compared to existing TVs based on the Internet. In order to provide more personalized services or information, it is necessary to analyze users' viewing patterns and provide customized services or information based on them. The proposed system receives the user's TV viewing pattern, analyzes it, and recommends a TV program or movie as customized information to the user. For this, the system was constructed with a preprocessor and a deep learning model. The preprocessor refines the name of the TV program watched by the user, the date the TV program was watched, and the watched time. Then, the multi-attribute LSTM model trains the refined data and performs prediction.The proposed system is a system that provides customized information to users, and is believed to be a leading technology in digital convergence that combines existing IoT technology and deep learning technology.

• Journal of Digital Contents Society
• /
• v.13 no.2
• /
• pp.197-203
• /
• 2012

Although the increase in number of the programs provides rich entertainment to viewers, it also has caused a negative of making it hard for viewers to find out their favorite programs. To address the problem, several researches have been performed mainly focusing on the technologies to analyze a viewer's TV watching-patterns and to recommend a program (or a channel) based on the analysis when a viewer changes channels. The researches, however, have the trouble of frequently failing to choose proper programs because, in the real-world broadcasting circumstance, the programs are re-broadcast over a number of the channels and a set of programs of a genre are usually playing in the overlapped times. To avoid the trouble, the data service, proposed in the paper, allows a viewer to book "explicitly" his/her favorite programs and provides a set of functions of listing up the booked program's broadcasting schedules and reserving viewing or recording the booked programs.

• Journal of Korea Multimedia Society
• /
• v.10 no.1
• /
• pp.128-138
• /
• 2007

With the development of broadcasting technology from analogue to interactive digital, the number of TV channels and contents provided to audience is increasing in a rapid speed. In this multi-media and multi-channel world, it is difficult to adapt to the increase of TV channel numbers and their contents merely using remote controller to search channels. Due to this reason, EPG (Electronic Program Guide) has been one of the essential services providing convenience to audience. So EPG complying with European DVB-MHP specifications, which will be also our domestic standard, is proposed in this paper. In order to provide audiences with DiTV contents they preferred, we apply collaborative filtering algorithm to recommend contents according to preference value of audience group with similar preference. And we use JavaXlet application which is based on MHP to implement this EPG, while the result can be verified by OpenMHP emulator.

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

• 프린팅코리아
• /
• s.7
• /
• pp.72-73
• /
• 2003

2002년 출판계의 화제는 '출판및인쇄진흥법' 제정, MBC-TV '!느낌표' 프로그램 추천도서의 베스트셀러 독식으로 상징되는 이른바 '매스컴셀러' 현상, 월드컵 기간 중의 종로서적 부도, 권위와 전통의 서평지 <출판저널> 휴간, 교양.오락.실용.아동 등 단행본 출판 전반의 매출 향상 등으로 집약된다.

• Journal of Korea Multimedia Society
• /
• v.12 no.2
• /
• pp.219-230
• /
• 2009

With the development of broadcasting technology from analogue to interactive digital, the number of TV channels and TV contents provided to audiences is increasing in a rapid speed. In this multi-channel world, it is difficult to adapt to the increase of the TV channel numbers and their contents merely using remote controller to search channels. For these reasons, the EPG system, one of the essential services providing convenience to audiences, is proposed in this paper. Collaborative filtering method with multi-step filtering is used in EPG to recommend contents according to the preference of audience groups with similar preference. To implement our designed TV contents recommendation EPG, we prefer DiTV and use JavaXlet programming based on MHP. The European DVB-MHP specification will be also our domestic standard in DiTV. Finally, the result is verified by OpenMHP emulator.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2004.05a
• /
• pp.1489-1492
• /
• 2004

본 논문은 디지털 방송을 수신하고 TV 를 통한 인터넷의 연결이 가능한 IP STB(Set Top Box)가 디지털 방송을 통해 전송되는 프로그램의 서비스 정보(Service Information)을 자동으로 분석하여, 시청자의 휴대 정보 단말 기기로 전송해 줌으로써, 시청자는 개인 휴대 정보 단말 기기를 통해 여러 매체을 통해 전송되는 프로그램의 시간표와 가이드를 볼 수 있으며, 해당 프로그램으로의 채널 변경을 휴대 정보 단말 기기를 통해 수행할 수 있다. 또한 본 논문은 시청자 개개인의 방송 시청 성향을 STB에서 분석하여, 개별 사용자가 전자 프로그램 가이드를 보기 위해 휴대 정보 단말 기기를 통해 STB에 연결되었을 때, 시청자의 성향분석에 기반을 한 추천 프로그램 목록을 제시함으로써, 다채널 다매체의 방송 환경에서 시청자가 원하는 프로그램을 놓치지 않고 시청 할 수 있도록 하며, 집 밖에서도 집안의 STB에 접속하여 자신이 원하는 프로그램을 휴대 정보 단말기기를 통해 예약 시청 또는 녹화가 가능하게 한다.

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

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.18 no.7
• /
• pp.1643-1651
• /
• 2014

Social networking services (in short, SNS) allow users to share their own data with family, friends, and communities. Since there are many kinds of information that has been uploaded and shared through the SNS, the amount of information on the SNS keeps increasing exponentially. Particularly, Facebook has adopted some interesting features related to entertainment (e.g., movie, music and TV show). However, they do not consider contextual information of users for recommendation (e.g., time, location, and social contexts). Therefore, in this paper, we propose a novel approach for movie recommendation based on the integration of a variety contextual information (i.e., when the users watched the movies, where the users watched the movies, and who watched the movie with them). Thus, we developed a Facebook application (called MyMovieHistory) for recording the movie history of users and recommending relevant movies.

• Journal of the Korea Society of Computer and Information
• /
• v.16 no.2
• /
• pp.207-215
• /
• 2011

Due to the increasing availability and popularity of digital television (DTV), the numbers of TV channels and programs that can be selected by consumers are also increasing rapidly. Therefore, searching for interesting channels and program via remote controls or channel guide maps can be frustrating and slow. In this paper, in order to better satisfy consumers, we propose a dynamic channel surfing scheme that reduces the channel seek distance in DTV. The proposed scheme dynamically rearranges the channel sequences according to the channel currently being watched to reduce the channel seek distance. The results of a simulation experiment demonstrate that the proposed dynamic channel surfing scheme reduces the channel seek distance for DTV channel navigation when up-down channel selection interfaces are used.