• 한국경영과학회지
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• 제29권2호
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• pp.59-75
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• 2004

Recommendation is a personalized information filtering technology to help customers find which products they would like to purchase. Collaborative filtering works by matching customer preferences to other customers in making recommendations. But collaborative filtering based recommendations have two major limitations, sparsity and scalability. To overcome these problems we suggest using adjusted product hierarchy, grain. This methodology focuses on dimensionality reduction and uses a marketer's specific knowledge or experience to improve recommendation quality. The qualify of recommendations using each grain is compared with others by several experimentations. Experiments present that the usage of a grain holds the promise of allowing CF-based recommendations to scale to large data sets and at the same time produces better recommendations. In addition. our methodology is proved to save the computation time by 3∼4 times compared with collaborative filtering.

• 한국산학기술학회:학술대회논문집
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• 한국산학기술학회 2003년도 Proceeding
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• pp.105-110
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• 2003

Collaborative filtering is one of the methodologies that are most widely used for recommendation system. It is based on a data matrix of each customer's preferences of products. There could be a lot of missing values in such preference. data matrix. This incomplete data is one of the reasons to deteriorate the accuracy of recommendation system. Multiple imputation method imputes m values for each missing value. It overcomes flaws of single imputation approaches through considering the uncertainty of missing values.. The objective of this paper is to suggest multiple imputation-based collaborative filtering approach for recommendation system to improve the accuracy in prediction performance. The experimental works show that the proposed approach provides better performance than the traditional Collaborative filtering approach, especially in case that there are a lot of missing values in dataset used for recommendation system.

• Journal of Information Processing Systems
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• 제15권3호
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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.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2013년도 추계학술발표대회
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• pp.1156-1157
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• 2013

The mobile application market has been growing extensively in recent years. Currently, Apple's App Store has more than 400,000 applications and Google's Android Market has above 150,000 applications. Such growth in volumes of mobile applications has created a need to develop a recommender system that assists the users to take the right choice, when searching for a mobile application. In this paper, we study the recommendation system building tools based on collaborative filtering. Specifically, we present a study on comparison analysis of collaborative filtering in Java and R statistical software. We implement the collaborative filtering using Java's Apache Mahout and R's recommenderlab package. We evaluate both methods and describe the advantages and disadvantages of using them in order to implement collaborative filtering.

• 한국전산유체공학회:학술대회논문집
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• 한국전산유체공학회 2005년도 춘계 학술대회논문집
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• pp.35-40
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• 2005

Up to recently, with the improvement of a computer power and high speed of network technology, advanced countries have researched a construction of the e-Science environment. As a major application part, a construction for environment of CFD, also, have studied together. During the research, people realize that not sharing hardware but also appropriate software development is really important to realize the environment. This paper describes about a construction of a collaborative research environment in the KISTI: Clients can connect to the computing resources through the web portal, run the Cactus simulation.: According to the computing resources, the simulation can migrate to some site to find better computing power.: Result of the calculation visualize at the web portal directly so that researchers of remote site can be share and analyze the result collaborative ways.

• 한국정보과학회:학술대회논문집
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• 한국정보과학회 2005년도 가을 학술발표논문집 Vol.32 No.2 (2)
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• pp.661-663
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• 2005

상업적인 추천 시스템에서 폭넓게 사용되고 있는 사용자 기반의 협력적 여과 방법 (User-Based Collaborative Filtering)은 확장성과 실시간 성능에 관련된 많은 제약을 갖는다. 이와 같은 맹점을 해결하기 위해 제안된 모델 기반의 협력적 여과 방법 (Model-Based Collaborative Filtering)은 추천은 매우 빠르지만, 모델을 구축하는 데 많은 시간이 소요되며, 사용자 기반의 협력적 여과 방법에 비해 추천의 질이 떨어지는 경향이 있다. 또한, 과거에 추천되있던 히스토리를 바탕으로 한 신뢰도 정보를 고려하는 추천 시스템은 추천의 정확도를 향상시키기 위한 다양한 연구 가운데 하나이다. 본 논문에서는 사용자 기반의 협력적 여과 방법의 문제점을 개선하고 추천의 정확도를 높이기 위해, 유사한 아이템의 모델을 미리 구축하는 아이템 기반의 협력적 여과 방법 (Item-Based Collaborative Filtering)에 각 아이템의 추천에 대한 신뢰도를 고려하여 보다 효율적인 추천 시스템을 제안하고자 한다. 또한, 기존 추천 시스템과의 성능 비교 실험을 통해 제안한 방법의 타당성을 제시한다.

• International Journal of Computer Science & Network Security
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• 제22권7호
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• pp.268-282
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• 2022

One of the most important technologies today is augmented reality technology, it allows users to experience the real world using virtual objects that are combined with the real world. This technology is interesting and has become applied in many sectors such as the shopping and medicine, also it has been included in the sector of education. In the field of education, AR technology has become widely used due to its effectiveness. It has many benefits, such as arousing students' interest in learning imaginative concepts that are difficult to understand. On the other hand, studies have proven that collaborative between students increases learning opportunities by exchanging information, and this is known as Collaborative Learning. The use of multimodal creates a distinctive and interesting experience, especially for students, as it increases the interaction of users with the technologies. The research aims at developing collaborative framework for developing achievement of 6th graders through designing a framework that integrated a collaborative framework with a multimodal input "hand-gesture and touch", considering the development of an effective, fun and easy to use framework with a multimodal interaction in AR technology that was applied to reformulate the genetics and traits lesson from the science textbook for the 6th grade, the first semester, the second lesson, in an interactive manner by creating a video based on the science teachers' consultations and a puzzle game in which the game images were inserted. As well, the framework adopted the cooperative between students to solve the questions. The finding showed a significant difference between post-test and pre-test of the experimental group on the mean scores of the science course at the level of remembering, understanding, and applying. Which indicates the success of the framework, in addition to the fact that 43 students preferred to use the framework over traditional education.

• Journal of Information Science Theory and Practice
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• 제8권4호
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• pp.55-66
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• 2020

The purpose of this study is to examine the relationship between collaborative information seeking and users' learning style preferences and their experience of information systems. The study investigates the role of four different factors including learning style, task complexity, and user experience in collaborative information seeking in digital environments. Sixty participants (30 pairs) were randomly chosen from volunteer graduate students of Kharazmi University (Iran). Participants completed Kolb's learning style questionnaire and a user experience questionnaire and then performed two information seeking tasks (one simple and one difficult) in a lab setting. They could exchange information with their partners or a librarian using Skype. The sessions were recorded using Camtasia. The results showed that with an increase in task difficulty, collaborative information seeking activities increased and more interactions with partners and the librarian occurred. The number of executive help-seeking requests was higher than the number of instrumental help-seeking requests. This research confirms that learning style is related to the way users interact with the digital library and help seeking. The research showed that in difficult tasks, the differences among users with different learning styles become more evident, and that generally interactions increase in more difficult tasks. Among the learning styles, the accommodating style had the highest number of relationships with collaborative information seeking variables. Most of the statistically significant relationships between users' prior computer experience and collaborative information seeking variables were related to the time variable.

• Asia pacific journal of information systems
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• 제30권1호
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• pp.214-228
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• 2020

Shared value enhances the competitiveness of a company while simultaneously reducing societal burdens. By allowing companies to share their resources, collaborative logistics systems provide companies with an opportunity to create shared value, namely, not only economic value by enhancing the utilization of resources, but also social value by reducing energy consumptions and greenhouse gas emissions associated with logistics and transportation. Emerging businesses, such as ES3 and Flexe, have recently demonstrated how they created shared value through collaborative logistics services, for example, ES3's collaborative warehousing and direct-to-store (D2S) program, and Flexe's on-demand warehousing platform. However, the development of collaborative logistics systems is currently at a nascent stage. There are quite a few socio-technical barriers to overcome for sharing resources (data as well as infrastructure). Drawing on the socio-technical approach, this research examines how companies create both economic and social value from collaborative logistics systems. We highlight socio-technical barriers, particularly one set of social barriers, that is, competition-oriented conservatism prevalent among companies. Using the case study methodology and interview data, we closely investigate ES3 and Flexe, which provide collaborative logistics services, and demonstrate how technical and social barriers are addressed to create shared value from collaborative logistics systems.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권3호
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• pp.1330-1347
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• 2018

Mobile Ad hoc Network (MANET) is a collection of nodes or communication devices that wish to communicate without any fixed infrastructure and predetermined organization of available links. The effort has been made by proposing a scheme to overcome the critical security issue in MANET. The insufficiency of security considerations in the design of Ad hoc On-Demand Distance Vector protocol makes it vulnerable to the threats of collaborative black hole attacks, where hacker nodes attack the data packets and drop them instead of forwarding. To secure mobile ad hoc networks from collaborative black hole attacks, we implement our scheme and considered sensor's energy as a key feature with a better packet delivery ratio, less delay time and high throughput. The proposed scheme has offered an improved solution to diminish collaborative black hole attacks with high performance and benchmark results as compared to the existing schemes EDRIAODV and DRIAODV respectively. This paper has shown that throughput and packet delivery ratio increase while the end to end delay decreases as compared to existing schemes. It also reduces the overall energy consumption and network traffic by maintaining accuracy and high detection rate which is more safe and reliable for future work.