• Proceedings of the Korean Operations and Management Science Society Conference
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• 2005.05a
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• pp.629-638
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• 2005

In this paper, we consider two issues in collaborative filtering, which are closely related with the number of ratings of a user. First issue is the relationship between the number of ratings of a user and the performance of collaborative filtering. The relationship is investigated with two datasets, EachMovie and Movielens datasets. The number of ratings of a user is critical when the number of ratings is small, but after the number is over a certain threshold, its influence on recommendation performance becomes smaller. We also provide an explanation on the relationship between the number of ratings of a user and the performance in terms of neighborhood formations in collaborative filtering. The second issue is how to select an initial product list for new users for gaining user responses. We suggest and analyze 14 selection strategies which include popularity, favorite, clustering, genre, and entropy methods. Popularity methods are adequate for getting higher number of ratings from users, and favorite methods are good for higher average preference ratings of users.

• 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.

• Cartoon and Animation Studies
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• s.16
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• pp.257-272
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• 2009

I analysis Alfonso Cuaron's comparing it with Charles Dickens' the original novel and David Lean's . In order to evaluate film which is based on classic novel, formal difference between film and novel, people's preference in those days and director's interpretations should be considered besides how the film follow the original novel faithfully. Regarding to these valuation basis, David Lean's film is true to the original novel and same time successful to add director's interpretation by using Mise en secne in it. Alfonso Cuaron is also succeed in filming well-made, people-like work by modifying the original novel with the convention of melo-drama genre and by using modern film form without breaking Hollywood classic film grammar.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.05a
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• pp.925-928
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• 2014

Currently, smart phone game's advances are going to more similar to the development of a computer games. there are many similar portions between an affinity for the genre of the game and the kind of developed game, which smart-phone technology is going to reach the computer's ability. In this paper, According to the pc online games and mobile game's graphics technology and the development of the network, there are analysis about An preference of the game by the user by the chronological situation, and about factors of results of preferred direction by the user.

• Journal of the Korean Society for information Management
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• v.38 no.2
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• pp.173-199
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• 2021

In this study, in order to understand the characteristics of the users' information needs for each public library unit, the user needs were analyzed based on the various attribute information of the books loaned to 11 public libraries in 8 districts in Seoul. As a result, the prominent book use patterns of the libraries were revealed, specifically related to the target user groups, purpose/motivation, interests/preferences, book genre, and subject. In addition, there was a preference for authors, and a difference in the role of the preferred authors in each library was also revealed. The results of this study will help to provide guidelines for the development of differentiated collections and service programs based on user needs.

• Proceedings of the Korea Information Processing Society Conference
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• 2022.05a
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• pp.346-348
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• 2022

Media Contents Recommendation Web Service (service name 'mobodra') is a web service that analyzes media types and genre tastes for each user and recommends content accordingly. Users select some of the works randomly provided on the web when signing up for membership and analyze their tastes based on this. Based on this analysis, preferred content for each user is recommended. In this paper, we implement a content recommendation algorithm through item-based collaborative filtering. When the user's activity data or preference is re-examined, the above process is executed again to update the user's taste.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.35 no.2
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• pp.47-64
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• 2024

This study was conducted to analyze students' school library use factors and purposes and to suggest ways to stimulate school library use. For this purpose, essays freely written by university students in the Department of Library and Information Science about the school library were analyzed and divided into personal factors, school library factors, use factors, and non-use factors. Comments on school library facilities, collections, school librarians, and programs were presented to promote school library use. In addition, it was suggested that the school library serve as a rest area, reflect student preference for comic books and genre novels, and strengthen club activities.

• Survey Research
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• v.13 no.1
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• pp.61-87
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• 2012

This research is a part of a fundamental research to form the topographic map of the South Korean society's cultural capital, based on large scale research data. Its purpose is to suggest suitable questions for today's Korean society as well as to compare with previous data accumulated from other nations. For this, this research is to establish theoretical background through critical study on the extensive literature on domestic and foreign cultural capital and collect measures, questionnaires, and data used in important literature and surveys. Based on this, the major domains and levels that should be dealt in the questionnaire were chosen, literature review was conducted for each field; experts were investigated in order to develop questions more suitable for the Korean society considering each domain and level, and qualitative research on the subjects were conducted. This research as seen through the above processes, music genres and culture activities were chosen as major domains, "high/popular" level and "consumption/production" level were chosen as items, and specific items were composed considering Korea's distinct characteristics. Each of these items combine and complement the three aspects of measuring cultural capital(preference, participation, perception), which have been used incoherently in previous researches in measuring the level of possession in cultural capital. This led to developing questions such as the level of liking each item(preference), the level of participating in each item(participation), the level of luxuriousness in each item(perception), and the level of stylishness in each item(perception). This research holds significance in that it critically examines the vast amount of questionnaires used in the past for cultural capital research, provides a large framework to find Korean cultural capital by adding items considering Korea's distinct characteristics, and provides groundwork to fill in the non-Western gap in the discussion of cultural capital, which has been based on the West.

• Journal of Intelligence and Information Systems
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• v.19 no.3
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• pp.25-44
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• 2013

Accessing movie contents has become easier and increased with the advent of smart TV, IPTV and web services that are able to be used to search and watch movies. In this situation, there are increasing search for preference movie contents of users. However, since the amount of provided movie contents is too large, the user needs more effort and time for searching the movie contents. Hence, there are a lot of researches for recommendations of personalized item through analysis and clustering of the user preferences and user profiles. In this study, we propose recommendation system which uses ontology based knowledge base. Our ontology can represent not only relations between metadata of movies but also relations between metadata and profile of user. The relation of each metadata can show similarity between movies. In order to build, the knowledge base our ontology model is considered two aspects which are the movie metadata model and the user model. On the part of build the movie metadata model based on ontology, we decide main metadata that are genre, actor/actress, keywords and synopsis. Those affect that users choose the interested movie. And there are demographic information of user and relation between user and movie metadata in user model. In our model, movie ontology model consists of seven concepts (Movie, Genre, Keywords, Synopsis Keywords, Character, and Person), eight attributes (title, rating, limit, description, character name, character description, person job, person name) and ten relations between concepts. For our knowledge base, we input individual data of 14,374 movies for each concept in contents ontology model. This movie metadata knowledge base is used to search the movie that is related to interesting metadata of user. And it can search the similar movie through relations between concepts. We also propose the architecture for movie recommendation. The proposed architecture consists of four components. The first component search candidate movies based the demographic information of the user. In this component, we decide the group of users according to demographic information to recommend the movie for each group and define the rule to decide the group of users. We generate the query that be used to search the candidate movie for recommendation in this component. The second component search candidate movies based user preference. When users choose the movie, users consider metadata such as genre, actor/actress, synopsis, keywords. Users input their preference and then in this component, system search the movie based on users preferences. The proposed system can search the similar movie through relation between concepts, unlike existing movie recommendation systems. Each metadata of recommended candidate movies have weight that will be used for deciding recommendation order. The third component the merges results of first component and second component. In this step, we calculate the weight of movies using the weight value of metadata for each movie. Then we sort movies order by the weight value. The fourth component analyzes result of third component, and then it decides level of the contribution of metadata. And we apply contribution weight to metadata. Finally, we use the result of this step as recommendation for users. We test the usability of the proposed scheme by using web application. We implement that web application for experimental process by using JSP, Java Script and prot$\acute{e}$g$\acute{e}$ API. In our experiment, we collect results of 20 men and woman, ranging in age from 20 to 29. And we use 7,418 movies with rating that is not fewer than 7.0. In order to experiment, we provide Top-5, Top-10 and Top-20 recommended movies to user, and then users choose interested movies. The result of experiment is that average number of to choose interested movie are 2.1 in Top-5, 3.35 in Top-10, 6.35 in Top-20. It is better than results that are yielded by for each metadata.

• 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.