• Venture DIGEST
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• no.1 s.126
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• pp.14-15
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• 2009

불황이다. 지금이야말로 벤처정신을 발휘해야 할 때다. 10년 전 IMF 위기를 벤처기업들이 앞장서서 돌파해 나갔듯이 다시 한 번 뭉쳐야 한다. 지금의 경제위기는 오히려 새로운 기회가 될 수도 있다. 이럴 때일수록 당당히 도전하는 진정한 벤처 정신이 빛을 발하기 때문이다. 독보적인 기술력 혹은 튀는 아이디어로 불황에도 대박을 터트리는 아이템이 있다. 불활에 강한 벤처 아이템들 어떤 것들이 있을까.

• Journal of Korea Game Society
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• v.19 no.3
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• pp.113-122
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• 2019

The study classifies online game users with two groups, which are users who purchase functional items and emotional items. This research used statistic tool, STATA MP, trying unpaired t-test to find relationships between two groups on payment intention and characteristics. The result shows that the users with functional items prefer community-oriented mind and interaction with other users while users with emotional items intend to consider item's design and superiority with higher purchasing satisfaction. The result of this study seems to give some implications to game companies when improving its purchasing system with charged items.

• Proceedings of the IEEK Conference
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• 2003.07d
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• pp.1471-1474
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• 2003

MPEG-21 에서 정의된 멀티미디어 프레임워크 하에서 디지털아이템(Digital Item)이 이용자(User) 상호간에 교환되어지고 이용되어지는 상황이 MPEG-21 이 추구하는 궁극적인 목표이다. 이러한 디지털 아이템의 교환과 이용은 IPMP(Intellectual Property Management and Protection), 콘텐츠 취급과 이용, 사건보고(Event Reporting), 효율적인 망과 단말 자원 관리 둥 다양하고 복잡한 활동들을 수반하며 디지털 아이템의 식별과 관련 정보 기술(Description)을 바탕으로 이런 활동들을 효율적으로 수행할 수 있다. 본 논문에서는 MPEG-21의 세 번째 부분인 디지털 아이템 식별(Digital Item Identification)의 일반적인 내용과 DⅡ RA(Registration Authority)에 대해 설명하고 타 식별체계 및 메타데이타 표준과의 관계와 응용분야에 대해에 대해 소개한다. 그리고 DⅡ의 개념을 이용한 식별시스템의 구성과 구축 방법에 대해 논의한다.

• Journal of the Korea Computer Industry Society
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• v.3 no.1
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• pp.1-8
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• 2002

This study is discussed research on management of items in PC's GUI(Graphical User Interface) environment. A items are general knowledge data like books, musical CD, English CD, and game CD, which are the time when we don't seek the right items in the case of re-reading and re-listening the items. In this paper, We propose an example designed to be used in the management of a items. The proposed example is implemented by educational VC＋＋(Visual C＋＋) programming language. This program and discussions for management of a items will understand the development procedure of searching and storing data, which will provide some basics into designing large database systems.

• Journal of Internet Computing and Services
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• v.14 no.6
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• pp.1-8
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• 2013

Data mining techniques are used to find important and meaningful information from huge databases, and pattern mining is one of the significant data mining techniques. Pattern mining is a method of discovering useful patterns from the huge databases. Frequent pattern mining which is one of the pattern mining extracts patterns having higher frequencies than a minimum support threshold from databases, and the patterns are called frequent patterns. Traditional frequent pattern mining is based on a single minimum support threshold for the whole database to perform mining frequent patterns. This single support model implicitly supposes that all of the items in the database have the same nature. In real world applications, however, each item in databases can have relative characteristics, and thus an appropriate pattern mining technique which reflects the characteristics is required. In the framework of frequent pattern mining, where the natures of items are not considered, it needs to set the single minimum support threshold to a too low value for mining patterns containing rare items. It leads to too many patterns including meaningless items though. In contrast, we cannot mine any pattern if a too high threshold is used. This dilemma is called the rare item problem. To solve this problem, the initial researches proposed approximate approaches which split data into several groups according to item frequencies or group related rare items. However, these methods cannot find all of the frequent patterns including rare frequent patterns due to being based on approximate techniques. Hence, pattern mining model with multiple minimum supports is proposed in order to solve the rare item problem. In the model, each item has a corresponding minimum support threshold, called MIS (Minimum Item Support), and it is calculated based on item frequencies in databases. The multiple minimum supports model finds all of the rare frequent patterns without generating meaningless patterns and losing significant patterns by applying the MIS. Meanwhile, candidate patterns are extracted during a process of mining frequent patterns, and the only single minimum support is compared with frequencies of the candidate patterns in the single minimum support model. Therefore, the characteristics of items consist of the candidate patterns are not reflected. In addition, the rare item problem occurs in the model. In order to address this issue in the multiple minimum supports model, the minimum MIS value among all of the values of items in a candidate pattern is used as a minimum support threshold with respect to the candidate pattern for considering its characteristics. For efficiently mining frequent patterns including rare frequent patterns by adopting the above concept, tree based algorithms of the multiple minimum supports model sort items in a tree according to MIS descending order in contrast to those of the single minimum support model, where the items are ordered in frequency descending order. In this paper, we study the characteristics of the frequent pattern mining based on multiple minimum supports and conduct performance evaluation with a general frequent pattern mining algorithm in terms of runtime, memory usage, and scalability. Experimental results show that the multiple minimum supports based algorithm outperforms the single minimum support based one and demands more memory usage for MIS information. Moreover, the compared algorithms have a good scalability in the results.

• Journal of the Korea Convergence Society
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• v.9 no.2
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• pp.137-143
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• 2018

This study is an interaction design study which can improve the usability of random item boxes that are used as in - game contents in Korean mobile games. Currently, Korean mobile game sales continue to rise. Sales of random item boxes are the main reason for the increase in sales. Problems with the use of random item boxes have been continuously raised, and legal regulations are currently in place. When using random item boxes, the user can not know the exact percentage information. There is a concern that this expresses gambling. In this context, we have studied interaction design to improve the usability of random item boxes, and conducted user online surveys and in - depth interviews to provide users with a better game experience. As a result, it is shown that providing percentage information intuitively when using random item boxes can enhance user experience. Through this study, it is expected that interaction design research will be actively conducted to provide a better user experience when using random item boxes.

• Journal of Korea Multimedia Society
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• v.12 no.3
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• pp.427-435
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• 2009

Users are able to search and read interesting items and hence click hyperlink linked to the item which is detailed content unit such as menu, login, news, video, etc. Small screen like mobile device is very difficult to viewing all web contents at once. Browsing and searching for interesting items by scrolling to left and right or up and down is discomfort to users in small screen. Searching and displaying directly the item preferred by users can reduces difficulty of interface manipulation of mobile device. To archive it, web contents based on desktop will be segmented on a per-item basis which component unit of web contents. Most segment algorithms are based on segment method through analysis of HTML code or mobile size. However, it is difficult to extract item blocks. Because present web content is getting more complicated and diversified in structure and content like web portal services. A web content segment algorithm suggested in this paper is based on extracting item blocks is component units of web contents.

• Journal of Intelligence and Information Systems
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• v.29 no.1
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• pp.249-263
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• 2023

Collaborative Filtering, a representative recommendation system methodology, consists of two approaches: neighbor methods and latent factor models. Among these, the latent factor model using matrix factorization decomposes the user-item interaction matrix into two lower-dimensional rectangular matrices, predicting the item's rating through the product of these matrices. Due to the factor vectors inferred from rating patterns capturing user and item characteristics, this method is superior in scalability, accuracy, and flexibility compared to neighbor-based methods. However, it has a fundamental drawback: the need to reflect the diversity of preferences of different individuals for items with no ratings. This limitation leads to repetitive and inaccurate recommendations. The Adaptive Deep Latent Factor Model (ADLFM) was developed to address this issue. This model adaptively learns the preferences for each item by using the item description, which provides a detailed summary and explanation of the item. ADLFM takes in item description as input, calculates latent vectors of the user and item, and presents a method that can reflect personal diversity using an attention score. However, due to the requirement of a dataset that includes item descriptions, the domain that can apply ADLFM is limited, resulting in generalization limitations. This study proposes a Generalized Adaptive Deep Latent Factor Recommendation Model, G-ADLFRM, to improve the limitations of ADLFM. Firstly, we use item ID, commonly used in recommendation systems, as input instead of the item description. Additionally, we apply improved deep learning model structures such as Self-Attention, Multi-head Attention, and Multi-Conv1D. We conducted experiments on various datasets with input and model structure changes. The results showed that when only the input was changed, MAE increased slightly compared to ADLFM due to accompanying information loss, resulting in decreased recommendation performance. However, the average learning speed per epoch significantly improved as the amount of information to be processed decreased. When both the input and the model structure were changed, the best-performing Multi-Conv1d structure showed similar performance to ADLFM, sufficiently counteracting the information loss caused by the input change. We conclude that G-ADLFRM is a new, lightweight, and generalizable model that maintains the performance of the existing ADLFM while enabling fast learning and inference.

• Digital Contents
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• no.6 s.61
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• pp.117-121
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• 1998

IP사업을 하려는 분들은 신문보기를 다이아몬드 같이 생각해야 좋은 아이템을 발굴할 수 있다. 지금 주위에서 돌아다니는 신문이 있다면 주의깊게 살펴보라! 다른사람의 눈에 띄지않는 사업아이템이 보일수도 있기 때문이다.

• 한국HCI학회:학술대회논문집
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• 2007.02a
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• pp.903-908
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• 2007

휴대용 모바일 디바이스의 입력 장치로는 스타일러스나 터치 스크린의 채용이 가장 일반적이나, 이들 방식을 사용하기 위해서는 사용자는 두 손을 모두 사용해야 하는 불편함이 있다. 이러한 문제점을 해결하기 위해 가속도나 자이로 센서를 이용한 tilt navigation이 많은 관심을 받고 있다. Tilt navigation은 한 손으로 모바일 디바이스를 잡고 기울임으로써 원하는 기능이나 항목을 선택할 수 있는 장점이 있으나, 사용자에게 익숙하지 않아 아직 보편화 되지 않고 있다. 본 연구는 tilt navigation에서 사용자가 원하는 메뉴나 항목을 보다 쉽게 찾아 갈 수 있도록 메뉴 또는 항목 간의 이동 속도를 제어하는 알고리즘을 제안하고자 한다. 제안하는 알고리즘은 목표 항목을 찾아 가는 과정에서 과거의 기울임 정보를 바탕으로 목표 아이템이 존재하는 범위를 설정하고, 항목 간의 이동 속도를 범위의 크기에 비례하도록 설정하여 사용자가 목표 아이템을 보다 편리하게 찾고 선택할 수 있도록 한다. 또한 본 연구에서는 가속도 센서를 장착한 PDA를 이용하여 기존의 tilt navigation과 제안하는 알고리즘이 적용된 tilt navigation과의 목표 아이템을 찾아가는데 걸리는 소요 시간을 비교하여, 제안하는 알고리즘의 효과를 보이고자 한다.