• The Journal of the Korea Contents Association
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• v.12 no.3
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• pp.461-469
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• 2012

This work was researched by practical method in a subjectivity study accessible in-depth, in sloughing off old habit of functional quantity analysis about reception type on movie-viewing perception. The perception pattern come out in this study were divided into four types in Q-methodology. The result is as follows ; it is that divided '1[(N=18): Personal-decision Type], 2[(N=14): Media Dependence Type], 3[(N=10): Self-leading Type], 4[(N=3): Positive Preference Type]'. Like this, it found that is very different type all over. Hereafter, this study is to ascertain acceptance behavior about reception type on movie-viewing perception, 21th ; to offer a developmental suggestion about it.

• Information Systems Review
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• v.9 no.1
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• pp.85-103
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• 2007

The purpose of this study is to evaluate the performance of collaborative filtering recommender algorithms for better prediction accuracy of the customer's preference. The accuracy of customer's preference prediction is compared through the MAE of neighborhood based collaborative filtering algorithm and correspondence mean algorithm. It is analyzed by using MovieLens 1 Million dataset in order to experiment with the prediction accuracy of the algorithms. For similarity, weight used in both algorithms, commonly, Pearson's correlation coefficient and vector similarity which are used generally were utilized, and as a result of analysis, we show that the accuracy of the customer's preference prediction of correspondence mean algorithm is superior. Pearson's correlation coefficient and vector similarity used in two algorithms are calculated using the preference rating of two customers' co-rated movies, and it shows that similarity weight is overestimated, where the number of co-rated movies is small. Therefore, it is intended to increase the accuracy of customer's preference prediction through expanding the number of the existing co-rated movies.

• Proceedings of the Korea Information Processing Society Conference
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• 2019.05a
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• pp.273-276
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• 2019

There is a lot of information in our world, quick access to the most accurate information or finding the information we need is more difficult and complicated. The recommendation system has become important for users to quickly find the product according to user's preference. A social recommendation system using community detection based on label propagation is proposed. In this paper, we applied community detection based on label propagation and collaborative filtering in the movie recommendation system. We implement with MovieLens dataset, the users will be clustering to the community by using label propagation algorithm, Our proposed algorithm will be recommended movie with finding the most similar community to the new user according to the personal propensity of users. Mean Absolute Error (MAE) is used to shown efficient of our proposed method.

• Journal of Internet Computing and Services
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• v.14 no.5
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• pp.59-67
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• 2013

In this paper, we propose the LAR_CF, latent attribute rating-based collaborative filtering, that is robust to data sparsity problem which is one of traditional problems caused of decreasing rating prediction accuracy. As compared with that existing collaborative filtering method uses a preference rating rated by users as feature vector to calculate similarity between objects, the proposed method improves data sparsity problem using unique attributes of two target objects with existing explicit preference. We consider MovieLens 100k dataset and its item attributes to evaluate the LAR_CF. As a result of artificial data sparsity and full-rating experiments, we confirmed that rating prediction accuracy can be improved rating prediction accuracy in data sparsity condition by the LAR_CF.

• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.5
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• pp.526-529
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• 2003

Recommender systems or collaborative filtering are methods to identify potentially interesting or valuable items to a particular user Under the assumption that people with similar interest tend to like the similar types of items, these methods use a database on the preference of a set of users and predict the rating on the items that the user has not rated. Usually the preference of a particular user is liable to vary with time and this temporal variation may cause an inaccurate identification and prediction. In this paper we propose a method to adapt the temporal variation of the user preference in order to improve the predictive performance of a collaborative filtering algorithm. To be more specific, the correlation weight of the GroupLens system which is a general formulation of statistical collaborative filtering algorithm is modified to reflect only recent similarity between two user. The proposed method is evaluated for EachMovie dataset and shows much better prediction results compared with GrouPLens system.

• The Journal of the Korea Contents Association
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• v.13 no.3
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• pp.54-64
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• 2013

This study is on Hollywood's latest romantic comedy movie , attempted to identify whether nationality or gender difference of the movie audience is affecting on their acceptance to the genre characteristics of Hollywood's romantic comedy. Most of Hollywood's romantic comedy movies are centered on attraction of female and male actors and its' formulated plot (boy-meets-girl, boy-loses-girl, boy-gets-girl structure). Although the movie is following the formulated romantic comedy plot, it shows a variation from the typical romantic comedy genre. In this thesis, based on audience research data, statistics and interrelationship analysis on audience value orientation, we elucidate how Korean and American, Female and Male movie audience have different preference to actors and characters in the movie and representation of archetypal Hollywood romantic comedy happy ending such as propose and wedding scenes.

• Journal of the Korean Society of Clothing and Textiles
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• v.34 no.12
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• pp.2006-2021
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• 2010

An authentic national spirit in media (particularly films), traditional images, and color preferences is expressed through movies that are melted in local traditions. This study suggests a direction regarding the characteristics for historical costumes by examining traditional color images and cultural preference in the Chinese film (1987), a representative film that deals with Chinese history and traditions. This movie can illustrate the correlation between the temporal backgrounds and the costumes in the movie with the criteria of Eastern color systems. The results of this research are summarized as follows. First, the image of Chinese traditional colors represented in many parts of and the cultural preferences that underlies their works through the expression of traditional colors. The scenes of traditional costumes and colors express the visual embodiments of the costumes that reflect a specific status, ceremony, or ritual. Second, the traditional colors used in the movie are based on the Yin-Yang theory. Particularly, Red, Yellow, Black is mostly used for ordinary clothing. Third, there are some differences in the use of color arrangements, that change regarding the use of traditional colors according to images and situations that follow the intention of the director. Planning the color arrangements is considered an engaging connectivity between traditions and images in the movie and it is extended or reduced based on cultural preferences. Fourth, the increase and decrease of color arrangements is distinctively represented as the story of the movie proceeds.

• Journal of Intelligence and Information Systems
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• v.18 no.2
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• pp.85-105
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• 2012

In recent years, IT advancement has brought out the new Internet communication environment such as online social network services, where people are connected in global network without temporal and spatial limitation. The popular use of online social network helps people share their experience and preference for specific products and services, thus holding large potential to significantly affect firms' business performance through Word-of-Mouth (WOM). This study examines the role of online social network in raising WOM effect on the movie industry by comparing with the similar role of Internet portal, another major online communication channel. Analyzing 109 movies and data from both Twitter and Naver movie, we found that significant WOM effect exists simultaneously in both Twitter and Naver movie. However, we also found that different figures of online viral effects exist depending on the popularity of movies. In the hit movie group, before the movie release, the WOM effect occurs only in Twitter while the WOM effect arises in both Twitter and Naver movie at the same time after the movie release. In the less-popular (or niche) movie group, the WOM effect occurs in both Twitter and Naver movie only before the movie release. Our findings not only deepen theoretical insights into different roles of the two online communication channels in provoking the WOM effect on entertainment products but also provide practitioners with incentive to utilize SNS as strategic marketing platform to enhance their brand reputations.

• Journal of Digital Convergence
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• v.18 no.12
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• pp.525-531
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• 2020

In today's IT environment where various pieces of information are distributed in large volumes, recommendation systems are in the spotlight capable of figuring out users' needs fast and helping them with their decisions. The current recommendation systems, however, have a couple of problems including that user preference may not be reflected on the systems right away according to their changing tastes or interests and that items with no relations to users' preference may be recommended, being induced by advertising. In an effort to solve these problems, this study set out to propose a Fuzzy-AHP-based movie recommendation system by applying the BRNN(Bidirectional Recurrent Neural Network) language model. Applied to this system was Fuzzy-AHP to reflect users' tastes or interests in clear and objective ways. In addition, the BRNN language model was adopted to analyze movie-related data collected in real time and predict movies preferred by users. The system was assessed for its performance with grid searches to examine the fitness of the learning model for the entire size of word sets. The results show that the learning model of the system recorded a mean cross-validation index of 97.9% according to the entire size of word sets, thus proving its fitness. The model recorded a RMSE of 0.66 and 0.805 against the movie ratings on Naver and LSTM model language model, respectively, demonstrating the system's superior performance in predicting movie ratings.

• The Journal of the Korea Contents Association
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• v.13 no.5
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• pp.191-198
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• 2013

This study examined the importance of independent variables that affect users' preference on the VODs of the newly-released movies by using the conjoint analysis. The results demonstrated that 'price(44.6%)' is the most important variable, which is followed by 'device(24.5%)', 'recency(18.0%)', 'producing country(12.9%)'. Respondents obtained the relatively high utility from watching the VOD of a Korean movie currently on screen in 3,000 wons. A television set was the most preferable device for watching VODs. The traditional windowing strategy in the media content market has been recently challenged by the new trend that the holdback period between market windows is ignored and the high-priced content is found in the second window. Nevertheless, this study demonstrated that users of the VODs of the newly-released movies are still very sensitive to the price and consider 'the recency' to be relatively less important than 'price'.