• Journal of Information Technology Services
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• v.22 no.1
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• pp.43-56
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• 2023

With the development of the e-commerce market, various types of products continue to be released. However, customers face an information overload problem in purchasing decision-making. Therefore, personalized recommendations have become an essential service in providing personalized products to customers. Recently, many studies on GCN-based recommender systems have been actively conducted. Such a methodology can address the limitation in disabling to effectively reflect the interaction between customer and product in the embedding process. However, previous studies mainly use implicit feedback data to conduct experiments. Although implicit feedback data improves the data scarcity problem, it cannot represent customers' preferences for specific products. Therefore, this study proposed a novel model combining explicit and implicit feedback to address such a limitation. This study treats the average ratings of customers and products as the features of customers and products and converts them into a high-dimensional feature vector. Then, this study combines ID embedding vectors and feature vectors in the embedding layer to learn the customer-product interaction effectively. To evaluate recommendation performance, this study used the MovieLens dataset to conduct various experiments. Experimental results showed the proposed model outperforms the state-of-the-art. Therefore, the proposed model in this study can provide an enhanced recommendation service for customers to address the information overload problem.

• Proceedings of the Korea Information Processing Society Conference
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• 2008.11a
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• pp.714-717
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• 2008

협업여과 추천기법에는 사용자 기반 협업여과와 아이템 기반 협업여과가 있으며, 절차는 유사도 측정, 이웃 선정, 예측값 생성 단계로 이루어진다. 유사도 측정 단계에는 유클리드 거리(Euclidean Distance), 코사인 유사도(Cosine Similarity), 피어슨 상관계수(Pearson Correlation Coefficient) 방법 등이 있고, 이웃 선정 단계에는 상관 한계치(Correlation-Threshold), 근접 N 이웃(Best-N-Neighbors) 방법 등이 있다. 마지막으로 예측값 생성 단계에는 단순평균(Simple Average), 가중합(Weighted Sum), 조정 가중합(Adjusted Weighted Sum) 등이 있다. 이처럼 협업여과 추천기법에는 다양한 기법들이 사용되고 있다. 따라서 본 논문에서는 사용자 기반 협업여과와 아이템 기반 협업여과 추천기법에 사용되는 유사도 측정 기법과 예측값 생성 기법의 최적화된 조합을 알아보기 위해 성능 실험 및 비교 분석을 하였다. 실험은 GroupLens의 MovieLens 데이터 셋을 활용하였고 MAE(Mean Absolute Error)값을 이용하여 추천기법을 비교 하였다. 실험을 통해 유사도 측정 기법과 예측값 생성 기법의 최적화된 조합을 찾을 수 있었고, 사용자 기반 협업여과와 아이템 기반 협업여과의 성능비교를 통해 아이템 기반 협업여과의 성능이 보다 우수했음을 확인 하였다.

• Journal of Internet Computing and Services
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• v.25 no.1
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• pp.49-56
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• 2024

There have been emerging many use-cases applying recommendation systems especially in online platform. Although the performance of recommendation systems is affected by a variety of factors, selecting appropriate features is difficult since most of recommendation systems have sparse data. Conventional matrix factorization (MF) method is a basic way to handle with problems in the recommendation systems. However, the MF based scheme cannot reflect non-linearity characteristics well. As deep learning technology has been attracted widely, a deep neural network (DNN) framework based collaborative filtering (CF) was introduced to complement the non-linearity issue. However, there is still a problem related to feature embedding for use as input to the DNN. In this paper, we propose an effective method using singular value decomposition (SVD) based feature embedding for improving the DNN performance of recommendation algorithms. We evaluate the performance of recommendation systems using MovieLens dataset and show the proposed scheme outperforms the existing methods. Moreover, we analyze the performance according to the number of latent features in the proposed algorithm. We expect that the proposed scheme can be applied to the generalized recommendation systems.

• Journal of Korea Entertainment Industry Association
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• v.13 no.8
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• pp.181-187
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• 2019

The theme of "youth love film" is a strong point in the Korean film industry and has made remarkable achievements in the overseas market. Jae-young Kwak is a famous Korean film director and screenwriter. He is good at shooting "youth love film" with beautiful pictures, exquisite emotions and rich imagination. His films have unique charm and characteristics. This paper expounds the image style, characterization and theme. First of all, from the perspective of image style for this paper, analysis of the film is how to use different narratives and lens language to create a movie's atmosphere, makes the transfer of drunk the oriental beauty. Secondly, the main focus is on the director to find a new way to shape the characters in the film, and pay attention to the description of details, foreshadowing and explaining the plot. Thirdly, based on the theme, this paper analyzes the profound meaning behind romantic films, which reflect the yearning and pursuit of modern people, and people should pay attention to and think deeply. In the modern economic globalization, countries culture mutual exchanges and cooperation, South Korea many outstanding movie worth exploring and research, by studying the Jae-young director Kwak youth love films, excavate its popular film in the market of reason, analysis of the works reflect the aesthetic characteristics, which sums up the experience of the youth love film.

• Journal of Broadcast Engineering
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• v.7 no.3
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• pp.211-218
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• 2002

This paper introduces the HD 3DTV camera and 3DTV receiver that are compatible with the ATSC HDTV broadcasting system. The developed 3DTV camera is based on stereoscopic techniques, and it has control function to control both left and right zoom lens simultaneously and to control the vergence. Moreover, in order to control the vergence manually and to eliminate the synchronization problem of the both images, the 3DTV camera has the 3DTV video multiplexing function to combine the left and right images into the single image. The developed 3DTV signal, and it has the various analog/digital interfaces. The performance of the developed system is confirmed by shooting the selected soccer game in 2002 FIFA KOREA/JAPANTM World Cup and by broadcasting the match. The HD 3DTV camera and receiver will be applied to the 3DTV industries such as 3D movie, 3D game, 3D image processing, 3DTV broadcasting system, and so on.

• The Journal of the Korea Contents Association
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• v.10 no.6
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• pp.236-248
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• 2010

Through this thesis, I analyzed Coen brothers's directing style centering around Crime movies: Blood simple, Barton Fink, Fargo, The man who wasn't thers, No country for old man. About directing style of Coen brothers, plot is an epic depiction, habitual use of narration, refusal of happy ending. They concern subject matters about human original nature and tragedy by greed. And as motifs, they often use misunderstanding, crossing over, chase. Also they likes characters of ordinary people who is destroyed by greed. How about their technical style? They usually use steady-cam and wide lens in camera, and in his early crime movies he used expressionistic lights, but after that they usually used realistic natural lights. About sound, they likes more sound effects than music. Also they directed realistic acting and emphasized actor's personality. Coen brothers has been directed many crime movies and comedy films. But their unique directing style was particularly outstanding in the crime movies

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.43 no.4 s.310
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• pp.50-57
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• 2006

In Electronic Commerce, the latest most of the personalized recommender systems have applied to the collaborative filtering technique. This method calculates the weight of similarity among users who have a similar preference degree in order to predict and recommend the item which hits to propensity of users. In this case, we commonly use Pearson Correlation Coefficient. However, this method is feasible to calculate a correlation if only there are the items that two users evaluated a preference degree in common. Accordingly, the accuracy of prediction falls. The weight of similarity can affect not only the case which predicts the item which hits to propensity of users, but also the performance of the personalized recommender system. In this study, we verify the improvement of the prediction accuracy through an experiment after observing the rule of the weight of similarity applying Vector similarity, Entropy, Inverse user frequency, and Default voting of Information Retrieval field. The result shows that the method combining the weight of similarity using the Entropy with Default voting got the most efficient performance.

• Journal of Information Science Theory and Practice
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• v.9 no.2
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• pp.66-82
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• 2021

Recommender Systems have gained immense popularity due to their capability of dealing with a massive amount of information in various domains. They are considered information filtering systems that make predictions or recommendations to users based on their interests and preferences. The more recent technology, Linked Open Data (LOD), has been introduced, and a vast amount of Resource Description Framework data have been published in freely accessible datasets. These datasets are connected to form the so-called LOD cloud. The need for semantic data representation has been identified as one of the next challenges in Recommender Systems. In a LOD-enabled recommendation framework where domain awareness plays a key role, the semantic information provided in the LOD can be exploited. However, dealing with a big chunk of the data from the LOD cloud and its integration with any domain datasets remains a challenge due to various issues, such as resource constraints and broken links. This paper presents the challenges of interconnecting and extracting the DBpedia data with the MovieLens 1 Million dataset. This study demonstrates how LOD can be a vital yet rich source of content knowledge that helps recommender systems address the issues of data sparsity and insufficient content analysis. Based on the challenges, we proposed a few alternatives and solutions to some of the challenges.

• Journal of Korea Entertainment Industry Association
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• v.15 no.5
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• pp.33-43
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• 2021

Kakao M and Kakao page have been merged to form contents corporation, Kakao entertainment. Kakao M has 15 contents management agencies and 4 music labels, in addition to movie and drama productions. Kakao Page currently holds IP rights for about 8,500 content stories. This study explores the relationship between M&A for absorptive capacity and content value chain by considering the factors that determine boundary spanning behaviors. Using the Kakao entertainment in-depth case study as the practical lens, research results of this study are suggested. Kakao's effective M&A activities are critical key factor for absorptive capacity in the entertainment industry and has a strong network with advantage assets. Also, as the contents business becomes even more competitive, Kakao need to venture beyond entertainment boundaries to seize creative opportunities. Kakao entertainment with absorptive capacity and boundary spanning behaviors through M&A and contents value chain best qualified for entertainment dominant strategy.

• Journal of Intelligence and Information Systems
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• v.17 no.1
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• pp.139-152
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• 2011

Recommender systems have become an important research field since the emergence of the first paper on collaborative filtering in the mid-1990s. In general, recommender systems are defined as the supporting systems which help users to find information, products, or services (such as books, movies, music, digital products, web sites, and TV programs) by aggregating and analyzing suggestions from other users, which mean reviews from various authorities, and user attributes. However, as academic researches on recommender systems have increased significantly over the last ten years, more researches are required to be applicable in the real world situation. Because research field on recommender systems is still wide and less mature than other research fields. Accordingly, the existing articles on recommender systems need to be reviewed toward the next generation of recommender systems. However, it would be not easy to confine the recommender system researches to specific disciplines, considering the nature of the recommender system researches. So, we reviewed all articles on recommender systems from 37 journals which were published from 2001 to 2010. The 37 journals are selected from top 125 journals of the MIS Journal Rankings. Also, the literature search was based on the descriptors "Recommender system", "Recommendation system", "Personalization system", "Collaborative filtering" and "Contents filtering". The full text of each article was reviewed to eliminate the article that was not actually related to recommender systems. Many of articles were excluded because the articles such as Conference papers, master's and doctoral dissertations, textbook, unpublished working papers, non-English publication papers and news were unfit for our research. We classified articles by year of publication, journals, recommendation fields, and data mining techniques. The recommendation fields and data mining techniques of 187 articles are reviewed and classified into eight recommendation fields (book, document, image, movie, music, shopping, TV program, and others) and eight data mining techniques (association rule, clustering, decision tree, k-nearest neighbor, link analysis, neural network, regression, and other heuristic methods). The results represented in this paper have several significant implications. First, based on previous publication rates, the interest in the recommender system related research will grow significantly in the future. Second, 49 articles are related to movie recommendation whereas image and TV program recommendation are identified in only 6 articles. This result has been caused by the easy use of MovieLens data set. So, it is necessary to prepare data set of other fields. Third, recently social network analysis has been used in the various applications. However studies on recommender systems using social network analysis are deficient. Henceforth, we expect that new recommendation approaches using social network analysis will be developed in the recommender systems. So, it will be an interesting and further research area to evaluate the recommendation system researches using social method analysis. This result provides trend of recommender system researches by examining the published literature, and provides practitioners and researchers with insight and future direction on recommender systems. We hope that this research helps anyone who is interested in recommender systems research to gain insight for future research.