• Journal of Computing Science and Engineering
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• v.9 no.2
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• pp.98-107
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• 2015

Hierarchical constraints and constraint satisfaction were analyzed in order to solve the problem of conflict detection in collaborative design. The constraints were divided into two sets: one set consisted of known constraints and the other of unknown constraints. The constraints of the two sets were detected with corresponding methods. The set of the known constraints was detected using an interval propagation algorithm, a back propagation (BP) neural network was proposed to detect the set with the unknown constraints. An immune algorithm (IA) was utilized to optimize the weights and the thresholds of the BP neural network, and the steps were designed for the optimization process. The results of the simulation indicated that the BP neural network that was optimized by IA has a better performance in terms of convergent speed and global searching ability than a genetic algorithm. The constraints were described using the eXtensible Markup Language (XML) for computers to be able to automatically recognize and establish the constraint network. The implementation of the conflict detection system was designed based on constraint satisfaction. A wind planetary gear train is taken as an example of collaborative design with a conflict detection system.

• Journal of KIISE:Computing Practices and Letters
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• v.7 no.6
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• pp.684-695
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• 2001

Recent recommender system uses a method of combining collaborative filtering system and content based filtering system in order to solve sparsity and first rater problem in collaborative filtering system. Collaborative filtering systems use a database about user preferences to predict additional topics. Content based filtering systems provide recommendations by matching user interests with topic attributes. In this paper, we describe a method for discovery of user preference through combining two techniques for recommendation that allows the application of machine learning algorithm. The proposed collaborative filtering method clusters user using genetic algorithm based on items categorized by Naive Bayes classifier and the content based filtering method builds user profile through extracting user interest using relevance feedback. We evaluate our method on a large database of user ratings for web document and it significantly outperforms previously proposed methods.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.26 no.2
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• pp.29-34
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• 2003

With the recent trends of electronic commerce moving from B2C to B2B, Collaborative Product Commerce (CPC) is widely adopted as a part of B2B in the virtual collaborative environment. This paper proposes an integrated CPC framework supporting collaborative works between businesses by sharing product data and collaborative process between global enterprises and their customers, who are related to product life cycle, and integrating application systems in order to realize a true B2B electronic commerce. To integrate into a loosely coupled structure, various web service technologies such as XML, SOAP, WSDL and UDDI are utilized as its fundamentals and on that basis, a CPC integration framework is implemented through the Internet to support real-time operation providing interoperability among all the different applications operated on various platforms which have been developed using different languages and adopted by different companies.

• Proceedings of the Korean Society of Computer Information Conference
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• 2020.07a
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• pp.279-280
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• 2020

최근 소셜 미디어로 이름을 알린 이색 카페와 맛집을 찾아다니는 문화가 확산되는 추세이다. 블로그 포털 검색을 통해 찾아본 맛집은 광고성 게시물이 많아서 신뢰도가 떨어지고, 맛집 관련 게시물 수가 많아서 모든 게시물들을 수동으로 읽기는 불가능하다. 본 논문에서는 사용자들이 선호해서 자발적으로 공유하는 신뢰도 높은 인스타그램의 맛집 포스트 데이터를 이용하여 아이템 기반의 협업 필터링(Item-based Collaborative Filtering) 기법을 통해 사용자의 취향에 맞고 선호할 만한 맛집을 자동으로 추천해주는 알고리즘 및 시스템을 소개한다.

• Journal of the Korean Society for Precision Engineering
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• v.16 no.10
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• pp.68-79
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• 1999

This paper describes an approach to collaborative design which focuses on the effects of individual activities on the overall design process. We utilize a new process modeling tool to define the process and then analyze and refine the process based on critical paths. This information is then shared over the Internet with all participants. The goal of this system is to detect critical errors at initial design stage and guide the designers to make better decisions based on the knowledge of the overall process. This system enables participating designers to publish his local process through an Internet bulletin board. Other members of the team can then provide feedback based on how the proposed process impacts their activities. The system provides a context-rich, persistent forum for collecting, preserving, and refining corporate expertise of the team. For example, designers can select any process from the bulletin board and use it as a template for his current project and then use it to maintain his own design history. This paper is based on the process modeling concepts of Design Roadamap and describe several key extensions in the area of CPM calculations and collaborative interfaces.

• Journal of the Korea Society of Computer and Information
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• v.23 no.4
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• pp.89-95
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• 2018

It is not easy for the user to find the information that is appropriate for the user among the suddenly increasing information in recent years. One of the ways to help individuals make decisions in such a lot of information is the recommendation system. Although there are many recommendation methods for such recommendation systems, a representative method is collaborative filtering. In this paper, we design and implement the movie recommendation system on user-based collaborative filtering of apache mahout based on mongoDB. In addition, Pearson correlation coefficient is used as a method of measuring the similarity between users. We evaluate Precision and Recall using the MovieLens 100k dataset for performance evaluation.

• Journal of Korean Institute of Industrial Engineers
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• v.36 no.3
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• pp.176-185
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• 2010

As long as the information systems are spread out, various efforts are being attempted to get the effective results by the system. As a result, a new management strategy has been appeared, referred to as "collaboration", and it is necessary to use a right methodology for modeling collaborative processes accordingly. Even though many modeling methodologies such as IDEF3, Petri-Nets, UML, and ARIS have been widely used for modeling processes, they are inadequate for clearly representing collaborative processes. Some researchers, therefore, have suggested new modeling methodologies for describing collaborative processes including CPM (collaborative process modeling). In this paper, we introduce an extended version of CPM method (i.e., exCPM) as a tool for modeling collaborative works after analyzing advantages and disadvantages of aforementioned methodologies. One of distinct characteristics of exCPM is that model verification is possible by transforming the exCPM models into Petri-Nets models. We also demonstrate transformation of an exCPM model in this paper with case studies for model verification.

• Journal of Internet Technology
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• v.19 no.6
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• pp.1691-1698
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• 2018

In this paper, we are to address the problem of item recommendations to users in shopping malls selling several different kinds of items, e.g., daily necessities such as cosmetics, detergent, and food ingredients. Most of current recommendation algorithms are developed for sites selling only one kind of items, e.g., music or movies. To devise efficient recommendation algorithms suitable for repetitively purchasing items, we give a method to implicitly assign ratings for these items by making use of repetitive purchase counts, and then use these ratings for the purpose of recommendation prediction with the help of user-based collaborative filtering and item-based collaborative filtering algorithms. We also propose associate item-based recommendation algorithm. Items are called associate items if they are frequently bought by users at the same time. If a user is to buy some item, it is reasonable to recommend some of its associate items. We implement user-based (item-based) collaborative filtering algorithm and associate item-based algorithm, and compare these three algorithms in view of the recommendation hit ratio, prediction performance, and recommendation coverage, along with computation time.

• Journal of the Korean Society for Library and Information Science
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• v.53 no.4
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• pp.361-372
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• 2019

The Overseas Construction Engineering Information System (OVICE), which is operated by the Korea Institute of Construction Technology, uses the Overseas Construction Information Service classification system to service information required for overseas construction of domestic construction engineering companies. In this study, through the application of the recommendation system using collaborative filtering, identifying the relationship between the subjects for real users, verifying the adequacy of the overseas construction information classification system currently in service. Through this, I would like to propose an information service classification system that reflects actual user demand.

• Journal of Intelligence and Information Systems
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• v.14 no.2
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• pp.157-177
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• 2008

Even in a single day, an enormous amount of content including digital videos, posts, photographs, and wikis are generated on the web. It's getting more difficult to recommend to a user what he/she prefers among these contents because of the difficulty of automatically grasping of content's meanings. CF (Collaborative Filtering) is one of useful methods to recommend proper content to a user under these situations because the filtering process is only based on historical information about whether or not a target user has preferred an item before. Collaborative Tagging is the process that allows many users to annotate content with descriptive tags. Recommendation using tags can partially improve, such as the limitations of CF, the sparsity and cold-start problem. In this research, a CF method with user-created tags is proposed. Collaborative tagging is employed to grasp and filter users' preferences for items. Empirical demonstrations using real dataset from del.icio.us show that our algorithm obtains improved performance, compared with existing works.