• Asia pacific journal of information systems
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• v.15 no.3
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• pp.223-241
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• 2005

In spite of the rapid growth of mobile multimedia contents market, most of the customers experience inconvenience, lengthy search processes and frustration in searching for the specific multimedia contents they want. These difficulties are attributable to the current mobile Internet service method based on inefficient sequential search. To overcome these difficulties, this paper proposes a MOBIIe COntents Recommender System for Movie(MOBICORS-Movie), which is designed to reduce customers' search efforts in finding desired movies on the mobile Internet. MOBICORS-Movie consists of three agents: CF(Collaborative Filtering), CBIR(Content-Based Information Retrieval) and RF(Relevance Feedback). These agents collaborate each other to support a customer in finding a desired movie by generating personalized recommendations of movies. To verify the performance of MOBICORS-Movie, the simulation-based experiments were conducted. The results from this experiments show that MOBICORS-Movie significantly reduces the customer's search effort and can be a realistic solution for movie recommendation in the mobile Internet environment.

• Journal of the Korea Convergence Society
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• v.11 no.6
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• pp.163-171
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• 2020

This study analyzed the contents and changes by year of metadata research that appeared in the study of domestic movie curation from the viewpoint of art management. The research method used thesis search site to search 'movie' and 'metadata' as keywords, and analyzed them in 4 stages of change according to the research trend by year, purpose of research content, analysis by use, and type of recommendation method. As for research results, movie metadata research is highly interested in user-side research, and is developing from an introduction stage to an evolutionary stage of recommendation to a sharing and participation stage. It was concluded that movie curation evolved into 6 stages: search support, content-based, collaborative filtering, hybrid, artificial intelligence, and curation.

• The Journal of the Korea Contents Association
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• v.13 no.12
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• pp.81-91
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• 2013

Social Media has been vitalized according to development of technology, it make the crowd-funding which have a form of new donation culture. The crowd-funding has been known as form that is supported for getting investments of ongoing or new project by much public in area of cultural art. Nowadays it receive attention from the movie content production. There are so many successful case such as , in abroad while it is hard to find distinct case in Korea' the movie content production market. Since the movie <26 years> informed public of 'the crowd-funding', recently was successfully complete first and second fund-raising and third fund-raising is in progress. It is upraised as a representative successful case.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.1
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• pp.82-88
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• 2017

Recently recommendation methods need personalization and diversity as well as accuracy whereas the traditional researches have been mainly focused on the accuracy of recommendation in terms of quality. The diversity of recommendation is also important to people in terms of quantity in addition to quality since people's desire for content consumption have been stronger rapidly than past. In this paper, we pay attention to similarity of data gathered simultaneously among different types of contents. With this motivation, we propose an enhanced recommendation method using correlation analysis with considering data similarity between two types of contents which are movie and music. Specifically, we regard folksonomy tags for music as correlated data of genres for movie even though they are different attributes depend on their contents. That is, we make result of new recommendation movie items through mapping music folksonomy tags to movie genres in addition to the recommendation items from the typical collaborative filtering. We evaluate effectiveness of our method by experiments with real data set. As the result of experimentation, we found that the diversity of recommendation could be extended by considering data similarity between music contents and movie contents.

• KIPS Transactions on Computer and Communication Systems
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• v.12 no.1
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• pp.25-40
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• 2023

Recommendation systems are frequently used by users to generate intelligent automatic decisions. In the study of movie recommendation system, the existing approach uses largely collaboration and content-based filtering techniques. Collaborative filtering considers user similarity, while content-based filtering focuses on the activity of a single user. Also, mixed filtering approaches that combine collaborative filtering and content-based filtering are being used to compensate for each other's limitations. Recently, several AI-based similarity techniques have been used to find similarities between users to provide better recommendation services. This paper aims to provide the prospective expansion by deriving possible solutions through the analysis of various existing movie recommendation systems and their challenges.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.1
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• pp.62-69
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• 2015

This study examined the effects of the level of narrativity of a culture content on the level of preference of a related culture content. The culture contents were categorized into novels, cartoons and TV programs according to the content type, and into dramas, comedies, and actions by the contents genre because previous studies found a high level of narrativity in novels and dramas. Based on the survey data on the movie preference, the following were found. First, when people prefer novels with high-level narrativity, rather than TV programs, which have low-level narrativity in a certain genre, they prefer watching movies in the same genre. Second, this relationship is even more reinforced when the genre of the original of the movie is drama, which has high-level narrativity, rather than comedies or actions, which have low-level narrativity. Narrativity plays an important role in the movie preference, especially when it comes to movie originals.

• Journal of Korea Entertainment Industry Association
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• v.13 no.6
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• pp.15-24
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• 2019

Hollywood traditional movie companies are continuously challenged by new-type companies represented by the streaming content platforms. Based on the latest market statistics, and with a multidimensional approach involving the production, distribution, projection and overseas market, etc, this paper analyzes the changes and development of North American movie industry in its process of coping with the globalization and digitalization of media in 2018, and summarizes the effective measures for Hollywood mainstream movie companies to adjust their own structures and operation mechanism to maintain their development in the environment of new media consumption.

• Asian Journal of Innovation and Policy
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• v.5 no.1
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• pp.78-91
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• 2016

To counter-attack against piracy, the movie industry is continuously developing new technologies for the protection of intellectual properties, only to find them instantly useless especially in the digital age. This study shifts the focus from technology to customer behavior, and analyzes customer behaviors vis-à-vis piracy using economic models. The theoretical model of optimal holdback strategy under the threat of piracy was derived and the result shows that holdback can be used as a tool not only for hedging the loss due to piracy, but also for reducing piracy. Based on the theoretical model, we suggested proper holdback strategy for each type of movie piracy.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.8
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• pp.3169-3181
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• 2015

Nowadays, online word-of-mouth has become a powerful influencer to marketing and sales in business. Opinion mining and sentiment analysis is frequently adopted at market research and business analytics field for analyzing word-of-mouth content. However, there still remain several challengeable areas for 1) sentiment analysis aiming for Korean word-of-mouth content in film market, 2) availability of machine learning models only using linguistic features, 3) effect of the size of the feature set. This study took a sample of 10,000 movie reviews which had posted extremely negative/positive rating in a movie portal site, and conducted sentiment analysis with four machine learning algorithms: naïve Bayesian, decision tree, neural network, and support vector machines. We found neural network and support vector machine produced better accuracy than naïve Bayesian and decision tree on every size of the feature set. Besides, the performance of them was boosting with increasing of the feature set size.

• Proceedings of the Korea Information Processing Society Conference
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• 2022.11a
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• pp.34-36
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• 2022

Over the last decades, information has increased exponentially due to SNS(Social Network Service), IoT devices, World Wide Web, and many others. Therefore, it was monumentally hard to offer a good service or set of recommendations to consumers. To surmount this obstacle numerous research has been conducted in the Data Mining field. Different and new recommendation models have emerged. In this paper, we proposed a Content-based movie recommendation system using demographic information of users and the average rating for genres. We used MovieLens Dataset to proceed with our experiment.