• Journal of the Korea Society of Computer and Information
• /
• v.18 no.10
• /
• pp.23-37
• /
• 2013

In this paper, the proposed movie recommender system constructs trust network, which is similar to social network, using user's trust information that users explicitly present. Recommendation on items is performed by using relation degree between users and information of recommended item is provided by a visualization method. We discover the hidden relationships via the constructed trust network. To provide visualized recommendation information, we employ Freebase which is large knowledge base supporting information such as movie, music, and people in structured format. We provide three visualization methods as the followings: i) visualization based on movie posters with the number of movies that user required. ii) visualization on extra information such as director, actor and genre and so on when user selected a movie from recommendation list. iii) visualization based on movie posters that is recommended by neighbors who a user selects from trust network. The proposed system considers user's social relations and provides visualization which can reflect user's requirements. Using the visualization methods, user can reach right decision making on items. Furthermore, the proposed system reflects the user's opinion through recommendation visualization methods and can provide rich information to users through LOD(Linked Open Data) Cloud such as Freebase, LinkedMDB and Wikipedia and so on.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2022.05a
• /
• pp.594-597
• /
• 2022

Recommendation systems are a prominent approach for users to make informed automated judgments. In terms of movie recommendation systems, there are two methods used; Collaborative filtering, which is based on user similarities; and Content-based filtering which takes into account specific user's activity. However, there are still issues with these two existing methods, and to address those, a combination of collaborative and content-based filtering is employed to produce a more effective system. In addition, various similarity methodologies are used to identify parallels among users. This paper focuses on a survey of the various tactics and methods to find solutions based on the problems of the current recommendation system.

• Journal of Advanced Information Technology and Convergence
• /
• v.9 no.2
• /
• pp.41-54
• /
• 2019

The cinema circuit is facing a digital, network, and mobile age, which expands non-theater accessibility to movies. Application platforms are situated as the most competitive business model that provide digital content such as games, music, books, and movies. Consumers can acquire content-related information not just offline, but online as well. Therefore, item information provided by application platforms is required. The information provided by application platforms consists of richly descriptive information such as storyline summary, consumer reviews, and related articles, while non-descriptive normative information covers data such as sales ranking, release date, genre, rental or purchase cost, domestic/foreign classification, consumer rating, number of consumer ratings, film rating, and so on. In this study, we surveyed and analyzed statistically the correlation between real-time sales ranking and other comparable non-descriptive information.

• Journal of Convergence for Information Technology
• /
• v.7 no.4
• /
• pp.121-127
• /
• 2017

In 2013, Baz Luhrmann's movie re-made Fitzgerald's novel, "The Great Gatsby". In Novel, readers keep trace of the plot with their imagination, but in Movie , movie director comes together to create visual and auditory elements of it. Daisy Buchanan is a fashion icon, wearing Prada, Chanel, and Tiffany's jewelry, which reproduce the costume of Jazz Age, and make viewers well understand that of Jazz Age. Symbols like "Ash Valley", "Green light", "East Egg", "West Egg" are presented more directly in movie. Roaring parties held in Gatsby's great mansion was made by computer graphic, and its enormous scale also reflects the mental chaos and the material affluence in those age. Additionally, actors excellent show highlights the theme of the novel. With the adaptation of novel, the film finally achieves more appealing art in front of the public. This thesis investigates these more logistically with the materials of internet.

• Journal of Intelligence and Information Systems
• /
• v.20 no.1
• /
• pp.49-65
• /
• 2014

Word of Mouth (WOM) is a behavior used by consumers to transfer or communicate their product or service experience to other consumers. Due to the popularity of social media such as Facebook, Twitter, blogs, and online communities, electronic WOM (e-WOM) has become important to the success of products or services. As a result, most enterprises pay close attention to e-WOM for their products or services. This is especially important for movies, as these are experiential products. This paper aims to identify the network factors of an online movie community that impact box office revenue using social network analysis. In addition to traditional WOM factors (volume and valence of WOM), network centrality measures of the online community are included as influential factors in box office revenue. Based on previous research results, we develop five hypotheses on the relationships between potential influential factors (WOM volume, WOM valence, degree centrality, betweenness centrality, closeness centrality) and box office revenue. The first hypothesis is that the accumulated volume of WOM in online product communities is positively related to the total revenue of movies. The second hypothesis is that the accumulated valence of WOM in online product communities is positively related to the total revenue of movies. The third hypothesis is that the average of degree centralities of reviewers in online product communities is positively related to the total revenue of movies. The fourth hypothesis is that the average of betweenness centralities of reviewers in online product communities is positively related to the total revenue of movies. The fifth hypothesis is that the average of betweenness centralities of reviewers in online product communities is positively related to the total revenue of movies. To verify our research model, we collect movie review data from the Internet Movie Database (IMDb), which is a representative online movie community, and movie revenue data from the Box-Office-Mojo website. The movies in this analysis include weekly top-10 movies from September 1, 2012, to September 1, 2013, with in total. We collect movie metadata such as screening periods and user ratings; and community data in IMDb including reviewer identification, review content, review times, responder identification, reply content, reply times, and reply relationships. For the same period, the revenue data from Box-Office-Mojo is collected on a weekly basis. Movie community networks are constructed based on reply relationships between reviewers. Using a social network analysis tool, NodeXL, we calculate the averages of three centralities including degree, betweenness, and closeness centrality for each movie. Correlation analysis of focal variables and the dependent variable (final revenue) shows that three centrality measures are highly correlated, prompting us to perform multiple regressions separately with each centrality measure. Consistent with previous research results, our regression analysis results show that the volume and valence of WOM are positively related to the final box office revenue of movies. Moreover, the averages of betweenness centralities from initial community networks impact the final movie revenues. However, both of the averages of degree centralities and closeness centralities do not influence final movie performance. Based on the regression results, three hypotheses, 1, 2, and 4, are accepted, and two hypotheses, 3 and 5, are rejected. This study tries to link the network structure of e-WOM on online product communities with the product's performance. Based on the analysis of a real online movie community, the results show that online community network structures can work as a predictor of movie performance. The results show that the betweenness centralities of the reviewer community are critical for the prediction of movie performance. However, degree centralities and closeness centralities do not influence movie performance. As future research topics, similar analyses are required for other product categories such as electronic goods and online content to generalize the study results.

• Proceedings of the Korean Society of Computer Information Conference
• /
• 2019.07a
• /
• pp.15-18
• /
• 2019

증가하는 메시징 앱 이용자 수, 언제 어디서나 시간과 장소에 제한받지 않는 고객 응대 서비스 그리고 대화형이라는 친근성 등은 챗봇(Chatbot)의 대표적인 장점들이다. 과거에는 단순히 패턴을 찾아 이미 설정된 기계적인 반응만을 했지만, 최근에는 여러 기술, 특히 AI 기술의 발달로 실생활에서도 도움을 줄 수 있는 챗봇들이 많아지기 시작했다. 본 논문에서는 인터넷이나 핸드폰 앱으로 영화 예매하는 것이 익숙하지 않은 사람이나 채팅을 하는 것이 익숙한 사람들이 신속, 편리하게 영화 예매를 할 수 있도록 지원하는 챗봇을 구현하고자 한다. 이를 위해 Dialogflow를 사용해서 예매자가 영화의 필수 정보들을 자연스러운 채팅을 통해 파악하도록 한다. 또한 영화와 관련된 키워드만 언급해도 되묻기 기능을 추가해 해당 영화를 유추할 수 있는 질의를 통해 정확한 예매를 지원한다.

• Journal of Information Technology and Architecture
• /
• v.9 no.3
• /
• pp.333-344
• /
• 2012

This paper proposes a system which recommends movies using information from social network services containing personal interest and taste. Method for establishing data is as follows. The system gathers movies' information from web sites and user's information from social network services such as Facebook and twitter. The data from social network services is categorized into six steps of emotion level for more accurate processing following users' emotional states. Gathered data will be established into vector space model which is ideal for analyzing and deducing the information with the system which is suggested in this paper. The existing similarity measurement method for movie recommendation is presentation of vector information about emotion level and similarity measuring method on the coordinates using Cosine measure. The deducing method suggested in this paper is two-phase arithmetic operation as follows. First, using general cosine measurement, the system establishes movies list. Second, using similarity measurement, system decides recommendable movie list by vector operation from the coordinates. After Comparative Experimental Study on the previous recommendation systems and new one, it turned out the new system from this study is more helpful than existing systems.

• Journal of Intelligence and Information Systems
• /
• v.22 no.3
• /
• pp.71-89
• /
• 2016

Sentiment analysis is used for identifying emotions or sentiments embedded in the user generated data such as customer reviews from blogs, social network services, and so on. Various research fields such as computer science and business management can take advantage of this feature to analyze customer-generated opinions. In previous studies, the star rating of a review is regarded as the same as sentiment embedded in the text. However, it does not always correspond to the sentiment polarity. Due to this supposition, previous studies have some limitations in their accuracy. To solve this issue, the present study uses a supervised sentiment classification model to measure a more accurate sentiment polarity. This study aims to propose an advanced sentiment classifier and to discover the correlation between movie reviews and box-office success. The advanced sentiment classifier is based on two supervised machine learning techniques, the Support Vector Machines (SVM) and Feedforward Neural Network (FNN). The sentiment scores of the movie reviews are measured by the sentiment classifier and are analyzed by statistical correlations between movie reviews and box-office success. Movie reviews are collected along with a star-rate. The dataset used in this study consists of 1,258,538 reviews from 175 films gathered from Naver Movie website (movie.naver.com). The results show that the proposed sentiment classifier outperforms Naive Bayes (NB) classifier as its accuracy is about 6% higher than NB. Furthermore, the results indicate that there are positive correlations between the star-rate and the number of audiences, which can be regarded as the box-office success of a movie. The study also shows that there is the mild, positive correlation between the sentiment scores estimated by the classifier and the number of audiences. To verify the applicability of the sentiment scores, an independent sample t-test was conducted. For this, the movies were divided into two groups using the average of sentiment scores. The two groups are significantly different in terms of the star-rated scores.

• Proceedings of the IEEK Conference
• /
• 2009.05a
• /
• pp.219-221
• /
• 2009

In this paper, we present stereoscopic conversion based on movie shot information. To overcome the low stereoscopic quality of automatic stereo conversion technologies, the usage of the shot type is expected to provide more satisfactory stereoscopic perception. In general, movie clips are produced with a variety of shot techniques such as long shot, closeup shot, medium shot, etc. Each shot has its own characteristics that can be utilized during the conversion process. Furthermore, description sceme for shot and camera information is presented in XML. XML shot editor generates XML shot data. and conversion module parses such data and converts 2D image into stereoscopic image.

• The Research Journal of the Costume Culture
• /
• v.16 no.6
• /
• pp.1050-1063
• /
• 2008

A movie is a fiction made on a basis of an author's and a writer's imagination, but all sorts of properties mixed with each other and most realistically expresses the era which becomes the background of a movie and acts as a carrier that connects designers with consumers. Thus, this study was carried out to review how the fashion products that designer's intention and commercial value added are expressed in collections by comparing and analysing the costumes in the movie "The Great Gatsby" that described the life of America's upper-class in 1920s and the 04 S/S Y&Kei collection which were proceeding after getting inspiration from this movie. For this, literature materials were inspected in order to make a theoretical review on social and cultural background and costumes history background in 1920s and the photo materials on movie costume were collected and analysed using DVD video captures, as well as the photo materials on 04 S/S Y&Kei were collected and analyzed through the institute providing domestic fashion information. The following conclusion was deduced through this study. First, in 1920s which becomes the background of this study, the slim shape of Flapper which looks like a young and boy became an ideal figure condition and the straight silhouette with low waist line and the short skirt that rose to knee was popular. Second, as a result of analysing movie costume by classifying it in silhouette, colors, and materials, straight silhouette of low waistline with a near colored - tone seen in the pastel series, including white, beige, pink, and gray was mainly constituted and the metal colors like silver and gold were used. As a material, chiffon, satin, velvet, flower patterned prints, and beads were used, which represented luxurious life of women in the upper classes. Third, as a result of comparing and analysing, it turned out that there was a similarity. However, in dress collection for a heroine, some dissimilarity differentiated from a movie costumes was found out in that the dresses in collection expressed moderate beauty and modernism and elegant beauty at the same time by matching a variety of materials and using black color.