• The Journal of Society for e-Business Studies
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• v.7 no.2
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• pp.129-141
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• 2002

This paper deals with the collaborative procurement process in a two-echelon supply chain that a manufacturing company and a contractor are connected through the information system. The real time information about inventory and production processing becomes available through this information system. We raise two issues related to the collaborative procurement process. First, we propose a VMI(Vendor-Managed Inventory) type of the procurement policy which focuses on the cost minimization for the total supply chain rather than individual companies. Second, based on the simulation study, we demonstrate that a collaborative procurement process is more cost-effective over classical procurement processes such as (Q, r). The result obtained in this paper can be applied to the quantitative evaluation of the cost saving effect when companies build the information sharing based, collaborative procurement system.

• Journal of the Korean Operations Research and Management Science Society
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• v.29 no.2
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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.

• The Journal of Korean Association of Computer Education
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• v.3 no.1
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• pp.23-30
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• 2000

The educational effect and practical use of collaborative learning produced in virtual learning communities are being discussed actively in these days. A higher-level interactive tool is essential for successful Internet-based collaborative learning through the network. In this paper, we designed and implemented an integrated browser which has the integrated learning environment to support collaborative learning, and thus the user interface of the network client(News, FTP, HTTP, SMTP, voice text chatting Clients) is improved. Therefore, the educational effect of Internet-based collaborative learning is get closer to that of face-to-face learning.

• Journal of the Korea Computer Graphics Society
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• v.9 no.2
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• pp.1-9
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• 2003

Information assurance(IA) refers to methodologies to protect engineering information by ensuring its availability, confidentiality, integrity, non-repudiation, authentication, access control, etc. In collaborative design, IA techniques are needed to protect intellectual property, establish security privileges and create "need to know" protections on critical features. Aside from 3D watermarking, research on how to provide IA to distributed collaborative engineering teams is largely non-existent. This paper provides a framework for information assurance within collaborative design, based on a technique we call role-based viewing. Such role-based viewing is achieved through integration of multi-resolution geometry and security models. 3D models are geometrically partitioned, and the partitioning is used to create multi-resolution mesh hierarchies. Extracting an appropriately simplified model suitable for access rights for individual designers within a collaborative design environment is driven by an elaborate access control mechanism.

• Proceedings of the KAIS Fall Conference
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• 2003.11a
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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 and communication convergence engineering
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• v.17 no.2
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• pp.135-141
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• 2019

Collaborative filtering algorithms often encounter data sparsity issues. To overcome this issue, auxiliary information of relevant items is analyzed and an item attribute matrix is derived. In this study, we combine the user-item attribute preference with the traditional similarity calculation method to develop an improved similarity calculation approach and use weights to control the importance of these two elements. A collaborative filtering algorithm based on user-item attribute preference is proposed. The experimental results show that the performance of the recommender system is the most optimal when the weight of traditional similarity is equal to that of user-item attribute preference similarity. Although the rating-matrix is sparse, better recommendation results can be obtained by adding a suitable proportion of user-item attribute preference similarity. Moreover, the mean absolute error of the proposed approach is less than that of two traditional collaborative filtering algorithms.

• Journal of Information Processing Systems
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• v.15 no.3
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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.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.9
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• pp.4684-4705
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• 2019

Collaborative filtering recommender systems are vulnerable to shilling attacks in which malicious users may inject biased profiles to promote or demote a particular item being recommended. To tackle this problem, many robust collaborative recommendation methods have been presented. Unfortunately, the robustness of most methods is improved at the expense of prediction accuracy. In this paper, we construct a robust Bayesian probabilistic matrix factorization model for collaborative filtering recommender systems by incorporating the detection of user anomaly rating behaviors. We first detect the anomaly rating behaviors of users by the modified K-means algorithm and target item identification method to generate an indicator matrix of attack users. Then we incorporate the indicator matrix of attack users to construct a robust Bayesian probabilistic matrix factorization model and based on which a robust collaborative recommendation algorithm is devised. The experimental results on the MovieLens and Netflix datasets show that our model can significantly improve the robustness and recommendation accuracy compared with three baseline methods.

• Proceedings of the Korea Information Processing Society Conference
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• 2013.11a
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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.

• International conference on construction engineering and project management
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• 2011.02a
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• pp.586-590
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• 2011

This paper presents the case study of the CE by collaborative system and proposes a model of the CM group for the cable supported bridge. The cable supported bridges have a large project scale and need a high level of construction method. Therefore an advanced construction management system is required for successful completion of project. The construction management (CM) group which control design management, construction plan, subcontract, technical support and R&D is organized for the cable supported bridge project. The CM group established a collaborative system with construction site and drew an effective management of cost, process, quality, safety for each project. Furthermore, the CM group established the procedure of construction management based on the construction engineering (CE) items and performed the project management on the construction phase. Efficiency of cost reduction and site control is maximized by using a collaborative system.