• Proceedings of the IEEK Conference
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• 2003.07d
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• pp.1451-1454
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• 2003

본 논문에서는 MPEG-21 에서 정의한 디지털아이템의 파일 포맷의 구조 및 방법을 멀티미디어 파일 포맷으로 제안하였다. 제안한 방법은 디지털아이템을 이진화하는 방법 및 기능, XML로 구성된 디지털아이템 선언(Digital Item Declaration)에서 미디어 리소스를 참조하는 방법 등이다. 제안한 멀티미디어 파일 포맷은 디지털방송, 무선통신, 인터넷 환경에서의 다양한 형태의 컨텐츠들을 효과적이고 체계적으로 조합할 수 있는 기능 및 효과적인 전송 기능을 제공하며, 향후 xml 기반 메타데이터와 미디어리소스를 포함하는 멀티미디어 컨텐츠의 파일 포맷에 대한 참고 모델로서 사용될 수 있다.

• Journal of Intelligence and Information Systems
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• v.11 no.1
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• pp.107-121
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• 2005

Recently, many researches on recommendation systems and collaborative filtering have been proceeding in both research and practice. However, although product items may have multi-valued attributes, previous studies did not reflect the multi-valued attributes. To overcome this limitation, this paper proposes new methodology for recommendation system. The proposed methodology uses multi-valued attributes based on clustering technique for items and applies the collaborative filtering to provide accurate recommendations. In the proposed methodology, both user clustering-based CBR and item attribute clustering-based CBR technique have been applied to the collaborative filtering to consider correlation of item to item as well as correlation of user to user. By using multi-valued attribute-based clustering technique for items, characteristics of items are identified clearly. Extensive experiments have been performed with MovieLens data to validate the proposed methodology. The results of the experiment show that the proposed methodology outperforms the benchmarked methodologies: Case Based Reasoning Collaborative Filtering (CBR_CF) and User Clustering Case Based Reasoning Collaborative Filtering (UC_CBR_CF).

• The Journal of the Korea Contents Association
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• v.7 no.6
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• pp.1-8
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• 2007

In electronic commerce, it is important for users to recommend the proper item among large item sets with saving time and effort. Therefore, if the recommendation system can be recommended the suitable item, we will gain a good satisfaction to the user. In this paper, we proposed the associative relation clustering split method in the collaborative filtering in order to perform the accuracy and the scalability. We produce the lift between associative items using the ratings data. and then split the node group that consists of the item to improve an efficiency of the associative relation cluster. This method differs the association about the items of groups. If the association of groups is filled, the reminding items combine. To estimate the performance, the suggested method is compared with the K-means and EM in the MovieLens data set.

• Journal of Korea Game Society
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• v.7 no.4
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• pp.125-134
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• 2007

There have been many researches on the characteristics of item trade. However, most researches were done by carrying out a survey and there has been few researches by analyzing the economy in online games. So, this article studied the cause of item trade by looking into the supply and demand of items in online games. The results shows that item trade is taking place mostly among only a few number of gamers who enjoy enchanting and who engage in castle sieges and blood pledge battles. The proportion of item trade by general garners is not so big. And major item providers are gold farms, not garners. Therefore, item trade is mostly carried out by a large number of garners who trade a small amount of sum and by a small number of heavy garners who trade large amount of sum. Understanding this characteristic of item trade would be a good ground for reviewing the policies and social effect regarding item trades.

• Journal of Korea Game Society
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• v.10 no.3
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• pp.47-59
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• 2010

Differing from most of current studies which recognizing game item cash-trade as simple static system, this study approaches game item cash-trade as Complex Adaptive System through historical analysis. The item-trade is a complex phenomenon converging between cyber-economy and real-economy, and production and consumption process of game-item are evolving dynamically over time. The results are following: first, the early item-trade emerges in endogenously rather than results from purposed system designed by singular actor. Second, after the early item-trade, the trade system as a CAS which various voluntary actors(single user, factory, game company, user community, agency, etc.) participates in is self-organizing for trading safety and efficiency. Third, the complex adaptive item-trade system satisfies actor's needs interdependently and accelerate positive feedback powerfully. This study implies that purposeful control disregarding emergent adaptive item-trade system distorts system efficiency and can lead to unintended policy failure.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.31 no.2B
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• pp.129-136
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• 2006

We propose a new data carousel scheduling algorithm for on-demand data broadcasting in DMB. The server divides data items into two sets named hot and cold, according to request statistics from clients. When constructing a data carousel, hot items are placed periodically with their upper limit of broadcasting frequency. If there are empty slots after placing hot items, cold items with high request ratio are placed until the carousel is full. A cold item is broadcast only once in the caroulsel. For the response on clients' requests, our proposed scheme is shown to have high success ratio with short waiting time.

• 한국HCI학회:학술대회논문집
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• 2008.02a
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• pp.688-692
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• 2008

All items from the 3d avatar system should be made to fit the avatar's physical form. However this method is not only a disadvantage in an economical perspective, but also it is difficult to satisfy the client's needs of avatar's variety form. To provide various forms of the avatars, the work load naturally increases. This research is about changing the 3d avatar's body shape based on 3d mesh morphing which allows the 3d avatar with smallest data possible. The result mesh could be generated from source and target mesh with the deformation ratio and all 3d items like hair style, pants, shoes and etc, which was made to fit to basic mesh also could be deformed automatically, to fit them to the result mesh as is. Even if the different physical avatar mesh body such as children style is added to 3d avatar system, it is not necessary to make the 3d avatar items which is fit to the new physical body, New avatar mesh body will be adopted to the 3d avatar system in real time.

• Proceedings of the Korea Information Processing Society Conference
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• 2006.11a
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• pp.51-54
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• 2006

본 논문에서는 각 사용자들의 취향에 맞는 음악을 추천하는 개인화된 음악 추천 시스템을 소개한다. 추천 시스템이란 사용자의 선호도를 분석하고 아이템들에 대한 사용자의 선호도를 예측하여 영화, 음악, 기사, 책, 웹 페이지 등과 같은 아이템들을 추천하는 시스템을 말한다. 추천 시스템들에서 가장 많이 사용하고 있는 협동적 추천 방식은 선호도 데이터를 기반으로 유사한 사용자들을 찾고, 유사 사용자들의 선호도를 기반으로 예측을 수행하는 것으로서, 여러 장점들이 있으나 희소성(sparsity) 문제와 확장성(scalability) 문제에 대해 취약점을 가지고 있다. 아이템들의 전체 수에 비해 매우 적은 수의 아이템 선호도 데이터만 존재한다면 사용자들의 유사도를 계산하기가 어려우며, 또한 사용자의 수가 늘어날수록 유사도 계산에 걸리는 시간이 급격하게 늘어남으로써 수백만 사용자가 있는 웹 사이트 등에서 실시간 추천을 수행하기 어렵다. 본 논문에서 소개하는 음악 추천 시스템은 이러한 문제점들을 해결하기 위해 그래프 기반 협동적 여과 기법을 사용한다. 그래프 기반 협동적 여과 기법은 기존의 협동적 여과 기법들과 달리 아이템들 사이의 연관관계를 그래프 모델로 표현하고 저장함으로써 묵시적인 선호도 정보들을 누적하여 희소성 문제를 해결하고, 추천 아이템을 선정하는데 필요한 계산 시간을 크게 단축하여 대규모 데이터에서 실시간 추천을 가능하게 한다는 장점이 있다.

• Journal of Intelligence and Information Systems
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• v.25 no.4
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• pp.89-103
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• 2019

Recently, various approaches to recommendation systems have been studied in terms of the quality of recommendation system. A recommender system basically aims to provide personalized recommendations to users for specific items. Most of these systems always recommend the most relevant items of users or items. Traditionally, the evaluation of recommender system quality has focused on the various predictive accuracy metrics of these. However, recommender system must be not only accurate but also useful to users. User satisfaction with recommender systems as an evaluation criterion of recommender system is related not only to how accurately the system recommends but also to how much it supports the user's decision making. In particular, highly serendipitous recommendation would help a user to find a surprising and interesting item. Serendipity in this study is defined as a measure of the extent to which the recommended items are both attractive and surprising to the users. Therefore, this paper proposes an application of serendipity measure to recommender systems to evaluate the performance of recommender systems in terms of recommendation system quality. In this study we define relevant or attractive unexpectedness as serendipity measure for assessing recommendation systems. That is, serendipity measure is evaluated as the measure indicating how the recommender system can find unexpected and useful items for users. Our experimental results show that highly serendipitous recommendation such as item-based collaborative filtering method has better performance than the other recommendations, i.e. user-based collaborative filtering method in terms of recommendation system quality.

• 한국경영정보학회:학술대회논문집
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• 2007.11a
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• pp.623-628
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• 2007

본 논문은 Web 2.0시대의 새로운 컨텐츠 매체로 각광받고 있는 블로그와 관련하여 태그 기반의 검색 알고리즘을 제안하고자 한다. 최근 블로그 검색과 관련하여 태그 기반의 블로그 검색 서비스가 등장하기 시작했지만, 현재 제공되는 태그 기반의 검색 서비스는 태그의 유무와 컨텐트의 최신성을 주요 기준으로 삼고, 태그와 컨텐트 간의 관련성을 제대로 고려하지 않아 검색 결과가 만존스럽지 못하는 경우가 많다. 따라서 본 논문에서는 태그와 컨텐트와의 관련성을 실수화하고 이를 주요 기준으로 검색 결과의 순위를 결정하는 PTRank 알고리즘을 제안하였다. PTRank 알고리즘에서는 1) 태그가 피드의 제목에 포함되었는지 여부, 2) 태그가 피드의 설명에 나타나는 회수, 3) 태그가 아이템의 제목에 포함되었는지 여부, 4) 태그가 아이템의 설명에 나타나는 횟수, 5) 피드 내에서 태그의 IDF값, 6) 사용자의 검색 행위를 이용해 태그와 컨텐트간의 관련성을 실수화하였다. 실험 결과, PTRank 모델 및 학습 알고리즘이 태그 기반의 피드 검색에서 잘 작동하며 검색에 효과적으로 활용될 수 있다는 것을 알 수 있었다.