• Journal of the Korea Society of Computer and Information
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• v.23 no.4
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• pp.89-95
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• 2018

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 based on mongoDB. 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.

• 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.

• 기호학연구
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• no.54
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• pp.177-203
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• 2018

On this study, we analyzed the movie With gods's narrative structure. This movie makes most people who has seen it feel deeply moved. We think that people can get deep feeling from a film at last after people who has seen the movie accept a context, story, and a message of the movie gladly. We focused on a special system in this movie With gods that can make give an ordinary message and big touch to people. Also we focused on every episodes of every space in a movie, and someone who moved freely between the spaces. A repetitive form and repetitive contents in a narrative become a special code itself -repetitiveness- for people and make them do auto-communication. Specifically, an information, the movie's repetitiveness, out of people become a special code for them, and then that make people bring memory and new information by themselves. Watching movie, people can look back up on life with every trial in the movie. In short, a repetitiveness and an auto-communication are a special system in the movie, that can make deeply touched.

• Journal of Korea Multimedia Society
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• v.12 no.11
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• pp.1572-1580
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• 2009

Combing through entire video files for the purpose of recognizing and retrieving matching movies requires much time and memory space. Instead, most current similar movie-matching methods choose to analyze only a part of each movie's video-image information. Yet, these methods still share a critical problem of erroneously recognizing as being different matching videos that have been altered only in resolution or converted merely with a different codecs. This paper proposes an audio-information-based search algorithm by which similar movies can be identified. The proposed method prepares and searches through a database of movie's spectral peak information that remains relatively steady even with changes in the bit-rate, codecs, or sample-rate. The method showed a 92.1% search success rate, given a set of 1,000 video files whose audio-bit-rate had been altered or were purposefully written in a different codec.

• Proceedings of the Korean Information Science Society Conference
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• 2003.04c
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• pp.386-388
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• 2003

We are developing a web-based movie recommender system that catches and reasons with user profiles and ratings to recommend movies. In the paper, we outline the current status of our implementation with particular emphasis on the mechanisms used to provide effective recommendations. Social recommender systems collect ratings of items from many individuals and use nearest-neighbor techniques to make recommendations to a user. However, these methods only depend on the ratings and ignore other useful information. Our primary concern is to provide an approach that can recommend the movies based on not only the user ratings but also the significant amount of other information that is available about the nature of each items - such as cast list or movie genre. We experimentally evaluate our approach and compare them to conventional social filtering, which suggests merits to our approach.

• Journal of Information Processing Systems
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• v.15 no.3
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• pp.616-631
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• 2019

With the rapid increase of information on the World Wide Web, finding useful information on the internet has become a major problem. The recommendation system helps users make decisions in complex data areas where the amount of data available is large. There are many methods that have been proposed in the recommender system. Collaborative filtering is a popular method widely used in the recommendation system. However, collaborative filtering methods still have some problems, namely cold-start problem. In this paper, we propose a movie recommendation system by using social network analysis and collaborative filtering to solve this problem associated with collaborative filtering methods. We applied personal propensity of users such as age, gender, and occupation to make relationship matrix between users, and the relationship matrix is applied to cluster user by using community detection based on edge betweenness centrality. Then the recommended system will suggest movies which were previously interested by users in the group to new users. We show shown that the proposed method is a very efficient method using mean absolute error.

• Journal of Fashion Business
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• v.18 no.4
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• pp.80-96
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• 2014

The purpose of this study is to consider the expression of costume through the review of cinema costumes and to provide the model of cooperation between fashion and movie industry by analysis of collaboration with fashion brands in the movie . The subjects are the 1974 film directed by Jack Clayton and the 2013 version by Baz Luhrmann. Cinema Fashion was studied by analyzing the costumes of the two main characters, Gatsby and Daisy, in each scene. Gatsby's costume appeared as a model of traditional American classic suit, sensitive G-G look that symbolizes social success and traditional casual style that reflects upper-class life style. Daisy's costume expressed pastel toned luxury flapper look, oriental art deco style, and prestigious jewelry representing high class. The collaborations with fashion brands were carried out with Ralph Lauren and Cartier in 1974 film, and Brooks Brothers, Prada, and Tiffany in 2013. The value of prestige brands that matched the images of the movie was utilized, but marketing strategies for the promotion of fashion goods were not enough in 1974 version. On the other hand, in 2013 film, the effects of collaboration of the movie and fashion brands were forecasted sufficiently and marketing campaigns for promotion were performed in a various ways. The characteristics of collaborations were as follows: (1) the usage of prestige brands value, (2) collections planning and promotion using the stories of a movie, (3) the usage of multidirectional digital media, and (4) multi-dimensional promotion using entertainment factors. In collaborations with the movie, fashion brands could make cooperative relationship to produce the positive effects for promotion and prestige image strategies and draw attention of the people to the movie and fashion.

• 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.

• Proceedings of the Korea Information Processing Society Conference
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• 2019.05a
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• pp.267-269
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• 2019

There are many recommendation systems offer an effort to get better preciseness the information to the users. In order to further improve more accuracy, the social network analysis method which is used to analyze data to community detection in social networks was introduced in the recommendation system and the result shows this method is improving more accuracy. In this paper, we propose a movie recommendation system using social network analysis and normalized discounted cumulative gain with the best accuracy. To estimate the performance, the collaborative filtering using the k nearest neighbor method, the social network analysis with collaborative filtering method and the proposed method are used to evaluate the MovieLens data. The performance outputs show that the proposed method get better the accuracy of the movie recommendation system than any other methods used in this experiment.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.05a
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• pp.389-391
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• 2023

In the era of Big Data, humanity is facing a huge overflow of information. To overcome such an obstacle, many new cutting-edge technologies are being introduced. The movie recommendation system is also one such technology. To date, many theoretical and practical kinds of research have been conducted. Our research also focuses on the movie recommendation system by implementing methods from Social Network Analysis(SNA) and Parallel Programming. We applied the Girvan-Newman algorithm to detect communities of users, and a future package to perform the parallelization. This approach not only tries to improve the accuracy of the system but also accelerates the execution time. To do our experiment, we used the MovieLense Dataset.