• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.615-617
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• 2022

Currently, the 'Gacha' system is becoming an indispensable profit generation method for online and mobile games. The system, also called "probability randomization," proceeds with cash-based payments, and it is not clear how much money you need to use to obtain the item you want. So, in response to the backlash of users, game companies introduced a "ceiling" system that allows users to get the items they want if they use it for more than a certain amount, and added several profit generation methods using it. We examine the impact of this system on continuous billing induction.

• Journal of Intelligence and Information Systems
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• v.28 no.3
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• pp.119-137
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• 2022

Recent recommendation system studies apply various deep learning models to represent user and item interactions better. One of the noteworthy studies is ONCF(Outer product-based Neural Collaborative Filtering) which builds a two-dimensional interaction map via outer product and employs CNN (Convolutional Neural Networks) to learn high-order correlations from the map. However, ONCF has limitations in recommendation performance due to the problems with CNN and the absence of side information. ONCF using CNN has an inductive bias problem that causes poor performances for data with a distribution that does not appear in the training data. This paper proposes to employ a Vision Transformer (ViT) instead of the vanilla CNN used in ONCF. The reason is that ViT showed better results than state-of-the-art CNN in many image classification cases. In addition, we propose a new architecture to reflect side information that ONCF did not consider. Unlike previous studies that reflect side information in a neural network using simple input combination methods, this study uses an independent auxiliary classifier to reflect side information more effectively in the recommender system. ONCF used a single latent vector for user and item, but in this study, a channel is constructed using multiple vectors to enable the model to learn more diverse expressions and to obtain an ensemble effect. The experiments showed our deep learning model improved performance in recommendation compared to ONCF.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.5
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• pp.715-721
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• 2018

With the spread of mobile devices, the users of social network services or e-commerce sites have increased dramatically, and the amount of data produced by the users has increased exponentially. E-commerce companies have faced a task regarding how to extract useful information from a vast amount of data produced by the users. To solve this problem, there are various studies applying big data processing technique. In this paper, we propose a collaborative filtering method that applies the tag weight in the Apache Spark platform. In order to elevate the accuracy of recommendation, the proposed method refines the tag data in the preprocessing process and categorizes the items and then applies the information of periods and tag weight to the estimate rating of the items. After generating RDD, we calculate item similarity and prediction values and recommend items to users. The experiment result indicated that the proposed method process large amounts of data quickly and improve the appropriateness of recommendation better.

• Journal of KIISE:Software and Applications
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• v.32 no.6
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• pp.542-553
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• 2005

Recommendation systems analyze user preferences and recommend items to a user by predicting the user's preference for those items. Among various kinds of recommendation methods, collaborative filtering(CF) has been widely used and successfully applied to practical applications. However, collaborative filtering has two inherent problems: data sparseness and the cold-start problems. If there are few known preferences for a user, it is difficult to find many similar users, and therefore the performance of recommendation is degraded. This problem is more serious when a new user is first using the system. In this paper we propose a method of integrating additional feature information of users and items into CF to overcome the difficulties caused by sparseness and improve the accuracy of recommendation. In our method, we first fill in unknown preference values by using the probability distribution of feature values, then generate the top-N recommendations by applying collaborative filtering on the modified data. We call this method of filling unknown preference values as data blurring. Several experimental results that show the effectiveness of the proposed method are also presented.

• The Magazine for Energy Service Companies
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• s.6
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• pp.28-29
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• 2000

''첫째는 해외시장을 무대로 경쟁할 수 있는 기술력이 관건입니다. 남들도 다하는 그런 기술이 아니라 세계적인 경쟁 가능성을 갖춘 기업을 적극 발굴하고 지원할 계획입니다. 또한, 대표이사의 추진력이나 됨됨이 등 자질 역시 핵심포인트입니다.''

• 건강소식
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• v.38 no.7
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• pp.32-33
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• 2014

날이 더우면 꽉 막힌 구두나 운동화보다 시원한 샌들에 눈이 가는 건 당연지사. 여름철 빼놓을 수 없는 패션 아이템으로 등극한 샌들을 좀 더 멋스럽고 건강하게 신는 방법을 알아보자.

• 프린팅코리아
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• v.12 no.7
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• pp.100-101
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• 2013

후지필름 공식 대리점인 성도GL이 공급하는 Acuity LED 1600은 드루파2012에서 소개된 후 국내에는 올해 초부터 본격적으로 공급되기 시작했다. 적은 투자비용으로도 넓은 범위의 프린팅 서비스 영역을 확대함으로써 기존고객을 유지하는 한편, 신규고객을 발굴할 수 있는 경쟁력 있는 제품으로 주목받고 있다.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.42 no.6
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• pp.63-74
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• 2005

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 the Institute of Electronics Engineers of Korea SD
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• v.43 no.2 s.344
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• pp.60-75
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• 2006

The MPEG-21 Multimedia Framework initiative aims to support a wide range of networks and devices in the delivery and consumption of multimedia resources. One of the primary goals of MPEG-21 is universal multimedia access (UMA) through Digital Item Adaptation (DIA), which supports multimedia streaming to heterogeneous devices ensurung the same readability and seamlessness. We pioneer power saving of luminal devices with MPEG-21 DIA, so that the MPEG-21 DIA can also be used to support power saving, even though the framework is not primarily designed for power reduction and only limited power awareness is defined by DIA. We introduce several power-saving techniques conforming to MPEG-21 DIA specifications and show the dependency relation among introduced techniques. We achieve energy savings of up to $66\%$ in hand-held multimedia devices with minor QoS (quality of service) degradation.

• 정보화사회
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• s.127
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• pp.74-75
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• 1999

제 1회 대학 정보통신 창업아이템경진대회 입상자의 실리콘밸리 견학지원 프로그램은 우리도 실리콘 밸리에 진출할 수 있다는 자신감을 심어준 기간이었다. 5박 6일의 코스는 한마디로 훈련이었다라고 할만큼 빡빡하게 채워진 일정들이었다. 그 기간동안 우리는 벤처사업을 하고 있는 여러 한국교포를 만나 그곳의 현황을 들을 수 있었다.