• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.13 no.4
• /
• pp.1525-1531
• /
• 2012

This study has been developed to make a marketing tool for word-of-mouth marketing for "Low-Budget Movie". By developing an application program of Low-Budget Movie for 20millions of Smart-Phone user, it can find out the best environment of service, function, etc. Hereby, it could contribute the progress of Korean film industry with this marketing tool of Low-Budget movie. For the research, conjoint analysis has been done through conjoint survey and general survey.

• Journal of the Korea Society of Computer and Information
• /
• v.16 no.4
• /
• pp.159-168
• /
• 2011

As e-commerce and social media service evolves, studies on recommender systems advance, especially concerning the application of collective intelligence to personalized custom service. With the development of smartphones and mobile environment, studies on customized service are accelerated despite physical limitations of mobile devices. A typical example is combined with location-based services. In this study, we propose a recommender system using movie genre similarity and preferred genres. A profile of movie genre similarity is generated and designed to provide related service in mobile experimental environment before prototyping and testing with data from MovieLens.

• The Journal of the Korea Contents Association
• /
• v.22 no.3
• /
• pp.430-441
• /
• 2022

With the development of ICT technology, various mobile applications have appeared one after another, and they have penetrated deeply into the lives of individuals. In order to explore the key factors that movie content-related applications should have through empirical studies, this study used the information system success model to investigate the influence of each quality factor on movie consumers' attitudes toward movies and their viewing intentions. As a result of the study, it was derived that the most important factor among the quality factors of movie content-related applications was the information quality factor, followed by the service quality being the next most important factor. In this study, based on the results of this study, the direction of application related to movie contents as a new distribution channel was proposed by suggesting a way to improve applications related to movie contents for the development of the domestic movie industry.

• Asia Marketing Journal
• /
• v.15 no.1
• /
• pp.43-55
• /
• 2013

Since movies are experience goods, consumers are easily influenced by other consumers' behavior. For moviegoers, box office rank is the most credible and easily accessible information. Many studies have found that the relationship between a movie's box office rank and its revenue departs from the Pareto distribution, and this phenomenon has been named "increasing returns to information." The primary objective of the current research is to apply the empirical model proposed by De Vany and Walls (1996) to the Korean movie market in order to examine whether the same phenomenon prevails in the Korean movie market. The other purpose of the present study is to provide managers with useful implications about the release timing of a movie by finding different curvatures that depend upon seasonality. The empirical test on the Korean movie market shows similar results as prior studies conducted on the U.S., Hong Kong, and U.K. movie markets. The phenomenon of increasing returns is generated by information transmission among consumers, which makes some movies become blockbusters and others bombs. The proposed model can also be interpreted in such a way that a change in the rank has a nonlinear effect on the movie's performance. If a movie climbs up the chart, it would be rewarded more than its proportion. On the other hand, if a movie falls down in the ranks, its performance would drop rapidly. The research result also indicates that the phenomenon of increasing returns occurs differently depending on when the movies are released. Since the tendency of the increasing returns to information is stronger during the peak seasons, movie marketers should decide upon the release timing of a movie based on its competitiveness. If a movie has substantial potential to incur positive word-of-mouth, it would be more reasonable to release the movie during the peak season to enjoy increasing returns. Otherwise, a movie should be released during the low season to minimize the risk of being dropped from the chart.

• The Korean Journal of Applied Statistics
• /
• v.35 no.2
• /
• pp.265-283
• /
• 2022

MovieLens data consists of recorded movie evaluations that was often used to measure the evaluation score in the recommendation system research field. In this paper, we provide additional information obtained by clustering user-specific genre preference information through movie evaluation data and movie genre data. Because the number of movie ratings per user is very low compared to the total number of movies, the missing rate in this data is very high. For this reason, there are limitations in applying the existing clustering methods. In this paper, we propose a convex clustering-based method using the pairwise fused penalty motivated by the analysis of MovieLens data. In particular, the proposed clustering method execute missing imputation, and at the same time uses movie evaluation and genre weights for each movie to cluster genre preference information possessed by each individual. We compute the proposed optimization using alternating direction method of multipliers algorithm. It is shown that the proposed clustering method is less sensitive to noise and outliers than the existing method through simulation and MovieLens data application.

• Journal of Digital Convergence
• /
• v.14 no.8
• /
• pp.453-460
• /
• 2016

This study aimed to compare the systems of ticketing application programs of three major movie theaters, CGV, Megabox, Lotte Cinema, with each other and evaluate the usability, and find problems to figure out user experiences for more convenient mobile ticketing. Experimental subjects with experiences of using apps of the movie theaters were recruited; First, as a primary task, they reserved movie tickets with each of the movie theater apps; Second, they had in-depth interviews with questions based on the model of Creating Pleasurable Interfaces by Stephen Anderson. As a result, users preferred the composition with information in order in overall, in which ticketing process went smoothly. In particular, users were more satisfied with convenient payment applications. Therefore, as an improving way, it is required to design an interface for users to recognize at a glance and a payment system within an app, not to design separate payment system out of the app. I hope this study will help actively conduct researches to maximize the usability in a way to reserve movie tickets through smartphone apps.

• Journal of the Korean Operations Research and Management Science Society
• /
• v.36 no.4
• /
• pp.161-184
• /
• 2011

As movie demands become more and more diversified, it is necessary for movie related firms to segment a whole heterogeneous market into a number of small homogeneous markets in order to identify the specific needs of consumer groups. Relevant market segmentation helps them to develop valuable offer to target segments through effective marketing planning. In this article, we introduce various segmentation methods and compare their advantages and disadvantages. In particular, we analyze "2009~2010 consumer survey data of Korean Film Industry" by using Latent Class Analysis(LCA), a statistical segmentation method which identifies exclusive set of latent classes based on consumers' responses to an observed categorical and numerical variables. It is applied PROC LCA, a new SAS procedure for conducting LCA and finally get the result of 11 distinctive clusters showing unique characteristics on their buying behaviors.

• Journal of the Korea Society of Computer and Information
• /
• v.22 no.7
• /
• pp.125-131
• /
• 2017

It is not easy for the user to find the information that is appropriate for the user among the suddenly increasing information in recent years. One of the ways to help individuals make decisions in such a lot of information is the recommendation system. Although there are many recommendation methods for such recommendation systems, a representative method is collaborative filtering. In this paper, we design and implement the movie recommendation system on user-based collaborative filtering of apache mahout. In addition, Pearson correlation coefficient is used as a method of measuring the similarity between users. We evaluate Precision and Recall using the MovieLens 100k dataset for performance evaluation.

• 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.

• The Korean Journal of Applied Statistics
• /
• v.35 no.1
• /
• pp.19-33
• /
• 2022

With the explosive growth of social media, its abundant text-based data generated by web users has become an important source for data analysis. For example, we often witness online movie reviews from the 'Naver Movie' affecting the general public to decide whether they should watch the movie or not. This study has conducted analysis on the Naver Movie's text-based review data to predict the actual ratings. After examining the distribution of movie ratings, we performed semantics analysis using Korean Natural Language Processing. This research sought to find the best review rating prediction model by comparing machine learning and deep learning models. We also compared various regression and classification models in 2-class and multi-class cases. Lastly we explained the causes of review misclassification related to movie review data characteristics.