• Journal of Intelligence and Information Systems
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• v.28 no.3
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• pp.1-22
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• 2022

We investigate the dynamic relationships among digital piracy activities, TV ratings, and online buzz for Korean TV dramas using a panel vector autoregression model. Our main findings include 1) TV ratings are negatively affected by digital piracy activities but positively affected by google buzz, 2) digital piracy activities are negatively affected by TV ratings and social buzz, and 3) social buzz and google buzz are positively influenced by each other. While many empirical studies were conducted to reveal the effects of music or movie piracy, our understanding of drama piracy is limited. We provide empirical evidence of the dynamic relationships between drama piracy, TV ratings, and online buzz. Our findings show the presence of indirect piracy effects on TV ratings through online buzz. Further, we reveal that social buzz and google trends play different roles in promoting TV ratings and piracy activities. We discuss the implications of our findings for theory and practitioners.

• Journal of The Korean Association of Information Education
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• v.25 no.6
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• pp.927-934
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• 2021

AI(Artificial intelligence) is gradually taking up a large part of our lives, and the pace of AI development is accelerating. It is called ACT that develop students' computational thinking in the way artificial intelligence learns. Among ACTs, pattern recognition is an essential factor in efficiently solving problems. Pattern analysis is part of the pattern recognition process. In fact, Netflix's personalized movie recommendation service and what it named Covid-19 after repeated symptoms are all the results of pattern analysis. While the importance of ACT, including pattern recognition, is highlighted, software education for kindergarten and elementary school lower grades is much insufficient compared to foreign countries. Therefore, this study aims to design and develop textbooks for the development of artificial intelligence-based computational thinking through pattern analysis for kindergarten students.

• Journal of Korea Society of Industrial Information Systems
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• v.15 no.2
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• pp.31-39
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• 2010

VoD(Video-on-Demand) servers have to provide timely processing guarantees for continuous media and reduce the storage and bandwidth requirements for continuous media. The compression techniques make the bit rates of compressed video data significantly variable from frame to frame. A VoD system should be able to provide the client with interactive operations such as fast forward and fast rewind in addition to normal playback of movie. However, interactive operations require additional resources such as storage space, disk bandwidth, memory and network bandwidth. In a stored video application such as VoD system, it is possible that a priori disk access patterns can be used to reserve the system resources in advance. In addition, clients of VoD server spend most of their time in playback mode and the period of time spent in interactive mode is relatively small. In this paper, I present the new buffer management scheme that provides efficient support for interactive operations in a VoD server using variable bit rate continuous media. Simulation results show that our strategy achieves 34% increase of the number of accepted clients over the LRU strategy.

• Journal of Intelligence and Information Systems
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• v.20 no.1
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• pp.133-148
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• 2014

Recently, with the advent of various information channels, the number of has continued to grow. The main cause of this phenomenon can be found in the significant increase of unstructured data, as the use of smart devices enables users to create data in the form of text, audio, images, and video. In various types of unstructured data, the user's opinion and a variety of information is clearly expressed in text data such as news, reports, papers, and various articles. Thus, active attempts have been made to create new value by analyzing these texts. The representative techniques used in text analysis are text mining and opinion mining. These share certain important characteristics; for example, they not only use text documents as input data, but also use many natural language processing techniques such as filtering and parsing. Therefore, opinion mining is usually recognized as a sub-concept of text mining, or, in many cases, the two terms are used interchangeably in the literature. Suppose that the purpose of a certain classification analysis is to predict a positive or negative opinion contained in some documents. If we focus on the classification process, the analysis can be regarded as a traditional text mining case. However, if we observe that the target of the analysis is a positive or negative opinion, the analysis can be regarded as a typical example of opinion mining. In other words, two methods (i.e., text mining and opinion mining) are available for opinion classification. Thus, in order to distinguish between the two, a precise definition of each method is needed. In this paper, we found that it is very difficult to distinguish between the two methods clearly with respect to the purpose of analysis and the type of results. We conclude that the most definitive criterion to distinguish text mining from opinion mining is whether an analysis utilizes any kind of sentiment lexicon. We first established two prediction models, one based on opinion mining and the other on text mining. Next, we compared the main processes used by the two prediction models. Finally, we compared their prediction accuracy. We then analyzed 2,000 movie reviews. The results revealed that the prediction model based on opinion mining showed higher average prediction accuracy compared to the text mining model. Moreover, in the lift chart generated by the opinion mining based model, the prediction accuracy for the documents with strong certainty was higher than that for the documents with weak certainty. Most of all, opinion mining has a meaningful advantage in that it can reduce learning time dramatically, because a sentiment lexicon generated once can be reused in a similar application domain. Additionally, the classification results can be clearly explained by using a sentiment lexicon. This study has two limitations. First, the results of the experiments cannot be generalized, mainly because the experiment is limited to a small number of movie reviews. Additionally, various parameters in the parsing and filtering steps of the text mining may have affected the accuracy of the prediction models. However, this research contributes a performance and comparison of text mining analysis and opinion mining analysis for opinion classification. In future research, a more precise evaluation of the two methods should be made through intensive experiments.

• Journal of the Korea Society of Computer and Information
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• v.16 no.11
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• pp.17-24
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• 2011

Collaborative filtering system is the clustering about user is built and then based on that clustering results will recommend the preferred item to the user. However, building user clustering is time consuming and also once the users evaluate and give feedback about the film then rebuilding the system is not simple. In this paper, genre pattern of movie recommendation systems is being used and in order to simplify and reduce time of rebuilding user clustering. A Frequent pattern networks is used and then extracts user preference genre patterns and through that extracted patterns user clustering will be built. Through built the clustering for all neighboring users to collaborative filtering is applied and then recommends movies to the user. When receiving user information feedback, traditional collaborative filtering is to rebuild the clustering for all neighbouring users to research and do the clustering. However by using frequent pattern Networks, through user clustering based on genre pattern, collaborative filtering is applied and when rebuilding user clustering inquiry limited by search time can be reduced. After receiving user information feedback through proposed user clustering based on genre pattern, the time that need to spent on re-establishing user clustering can be reduced and also enable the possibility of traditional collaborative filtering systems and recommendation of a similar performance.

• Journal of the Korean Society for Computer Game
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• v.31 no.4
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• pp.61-70
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• 2018

In this paper, we propose a synchronized playback method of 3D reference model and video by extracting golf swing information from learner's golf video to precisely compare and analyze each motion in each position and time in the golf swing, and present the implementation result. In order to synchronize the 3D model with the learner's swing video, the learner's golf swing movie is first photographed and relative time information is extracted from the photographed video according to the position of the golf club from the address posture to the finishing posture. Through applying time information from learners' swing video to a 3D reference model that rigs the motion information of a pro-golfer's captured swing motion at 120 frames per second through high-quality motion capture equipment into a 3D model and by synchronizing the 3D reference model with the learner's swing video, the learner can correct or learn his / her posture by precisely comparing his or her posture with the reference model at each position of the golf swing. Synchronized playback can be used to improve the functionality of manually adjusting system for comparing and analyzing the reference model and learner's golf swing. Except for the part where the image processing technology that detects each position of the golf posture is applied, It is expected that the method of automatically extracting the time information of each location from the video and of synchronized playback can be extended to general life sports field.

• Journal of Intelligence and Information Systems
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• v.17 no.3
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• pp.147-168
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• 2011

As personalized recommendation of products and services is rapidly growing in importance, a number of studies provided fundamental knowledge and techniques for developing recommendation systems. Among them, the CF technique has been most widely used and has proven to be useful in many practices. However, current collaborative filtering (CF) technique has still considerable rooms for improving the effectiveness of recommendation systems: 1) a similarity function most systems use to find so-called like-minded people is not well defined in that similarity is computed from a single perspective of similarity concept; and 2) temporal information that contains the changing preference of customers needs to be taken into account when making recommendations. We hypothesize that integration of multiple aspects of similarity and utilization of temporal information will improve the accuracy of recommendations. The objective of this paper is to test the hypothesis through a series of experiments using MovieLens data. The experimental results show that the proposed recommendation system highly outperforms the conventional CF-based systems, confirming our hypothesis.

• Journal of Korea Entertainment Industry Association
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• v.13 no.2
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• pp.79-91
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• 2019

In the entertainment industry, risk management is crucial for securing competitiveness due to the risk of investment. The competitiveness of contents is reinforced when external factors such as industrial environment and internal factors centering on involving mechanism are simultaneously provided. The involving mechanism is a form of cognitive response behavior of the audience and occurs through signal processing of the brain when watching the image contents. The signal processing of the brain related to the contents watching is mainly performed in the working memory area, and in the case of the captivating movie, the information other than the contents transmitted to the audience is blocked to generate a temporary dissociation state. A dissociation state similar to a symptom such as hypnosis or amnesia occurs when the audience's level of involving is high. On the other hand, contents information in which the audience is concentrating his attention is used intensively for constructing future thinking through an episodic buffer while the inflow of external information is relatively blocked or delayed. The spectator's future thinking configuration takes the form of a hypothesis-forming activity and is based on the predictability of the brain. When these hypothesized behaviors correspond to the problem solving simulation of story and predictability which is an evolutionary function of the brain, the audience' s brain is involved in the contents at a high level. In order for the act to be effective, the factors such as the background of the hypothesis, the subject of the hypothesis, the internal information of the person, the type and position and quantity of the hypothesis information, and the hypothesis relevance and type of information are important. Based on these factors, analysis of the Kim Eun Sook Drama 'Goblin' shows that the above elements are operated in a very organic and meaningful way.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.219-239
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• 2019

As the importance of providing customized services to individuals becomes important, researches on personalized recommendation systems are constantly being carried out. Collaborative filtering is one of the most popular systems in academia and industry. However, there exists limitation in a sense that recommendations were mostly based on quantitative information such as users' ratings, which made the accuracy be lowered. To solve these problems, many studies have been actively attempted to improve the performance of the recommendation system by using other information besides the quantitative information. Good examples are the usages of the sentiment analysis on customer review text data. Nevertheless, the existing research has not directly combined the results of the sentiment analysis and quantitative rating scores in the recommendation system. Therefore, this study aims to reflect the sentiments shown in the reviews into the rating scores. In other words, we propose a new algorithm that can directly convert the user 's own review into the empirically quantitative information and reflect it directly to the recommendation system. To do this, we needed to quantify users' reviews, which were originally qualitative information. In this study, sentiment score was calculated through sentiment analysis technique of text mining. The data was targeted for movie review. Based on the data, a domain specific sentiment dictionary is constructed for the movie reviews. Regression analysis was used as a method to construct sentiment dictionary. Each positive / negative dictionary was constructed using Lasso regression, Ridge regression, and ElasticNet methods. Based on this constructed sentiment dictionary, the accuracy was verified through confusion matrix. The accuracy of the Lasso based dictionary was 70%, the accuracy of the Ridge based dictionary was 79%, and that of the ElasticNet (${\alpha}=0.3$) was 83%. Therefore, in this study, the sentiment score of the review is calculated based on the dictionary of the ElasticNet method. It was combined with a rating to create a new rating. In this paper, we show that the collaborative filtering that reflects sentiment scores of user review is superior to the traditional method that only considers the existing rating. In order to show that the proposed algorithm is based on memory-based user collaboration filtering, item-based collaborative filtering and model based matrix factorization SVD, and SVD ++. Based on the above algorithm, the mean absolute error (MAE) and the root mean square error (RMSE) are calculated to evaluate the recommendation system with a score that combines sentiment scores with a system that only considers scores. When the evaluation index was MAE, it was improved by 0.059 for UBCF, 0.0862 for IBCF, 0.1012 for SVD and 0.188 for SVD ++. When the evaluation index is RMSE, UBCF is 0.0431, IBCF is 0.0882, SVD is 0.1103, and SVD ++ is 0.1756. As a result, it can be seen that the prediction performance of the evaluation point reflecting the sentiment score proposed in this paper is superior to that of the conventional evaluation method. In other words, in this paper, it is confirmed that the collaborative filtering that reflects the sentiment score of the user review shows superior accuracy as compared with the conventional type of collaborative filtering that only considers the quantitative score. We then attempted paired t-test validation to ensure that the proposed model was a better approach and concluded that the proposed model is better. In this study, to overcome limitations of previous researches that judge user's sentiment only by quantitative rating score, the review was numerically calculated and a user's opinion was more refined and considered into the recommendation system to improve the accuracy. The findings of this study have managerial implications to recommendation system developers who need to consider both quantitative information and qualitative information it is expect. The way of constructing the combined system in this paper might be directly used by the developers.

• Journal of Internet Computing and Services
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• v.17 no.3
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• pp.107-113
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• 2016

Following the recent advancement in the use of social networks, a vast amount of different online reviews is created. These variable online reviews which provide feedback data of contents' are being used as sources of valuable information to both contents' users and providers. With the increasing importance of online reviews, studies on opinion mining which analyzes online reviews to extract opinions or evaluations, attitudes and emotions of the writer have been on the increase. However, previous sentiment analysis techniques of opinion-mining focus only on the classification of reviews into positive or negative classes but does not include detailed information analysis of the user's satisfaction or sentiment grounds. Also, previous designs of the sentiment analysis technique only applied to one content domain that is, either product or movie, and could not be applied to other contents from a different domain. This paper suggests a sentiment analysis technique that can analyze detailed satisfaction of online reviews and extract detailed information of the satisfaction level. The proposed technique can analyze not only one domain of contents but also a variety of contents that are not from the same domain. In addition, we design a system based on Hadoop to process vast amounts of data quickly and efficiently. Through our proposed system, both users and contents' providers will be able to receive feedback information more clearly and in detail. Consequently, potential users who will use the content can make effective decisions and contents' providers can quickly apply the users' responses when developing marketing strategy as opposed to the old methods of using surveys. Moreover, the system is expected to be used practically in various fields that require user comments.