• Proceedings of the Korea Inteligent Information System Society Conference
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• 2007.11a
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• pp.602-609
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• 2007

Combining collaborative filtering with some other technique is most common in hybrid recommender systems. As many recommended items from collaborative filtering seem to be similar with respect to content, the collaborative-content hybrid system suffers in terms of quality recommendation and recommending new items as well. To alleviate such problem, we have developed a novel method that uses a diversity metric to select the dissimilar items among the recommended items from collaborative filtering, which together with the input when fed into content space let us improve and include new items in the recommendation. We present experimental results on movielens dataset that shows how our approach performs better than simple content-based system and naive hybrid system

• Journal of Smart Tourism
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• v.4 no.1
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• pp.19-29
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• 2024

The objective of this article is to create a model of integrated management from the framework modeling of a digital collaborative network supported by knowledge engineering to make heritage site in the Brazil more effective. It is an exploratory and qualitative research with thematic analysis as technique of data analysis from the collaborative network, digital platform, world heritage, and tourism themes. The snowballing approach was chosen, and the mapping and classification of relevant studies was developed with the use of the spreadsheet tool and the Mendeley® software. The results show that the collaborative network model oriented towards strategic objectives should be supported by a digital platform that provides a technological environment that adds functionalities and digital platform services with the integration of knowledge engineering techniques and tools, enabling the discovery and sharing of knowledge in the collaborative network.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1999-2005
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• 2005

Collaboration in wireless sensor networks must be fault-tolerant due to the harsh environmental conditions in which such networks can be deployed. This paper focuses on finding signal processing algorithms for collaborative target detection based on the generalized approach to signal processing in the presence of noise that are efficient in terms of communication cost, precision, accuracy, and number of faulty sensors tolerable in the wireless sensor network. Two algorithms, namely, value fusion and decision fusion constructed according to the generalized approach to signal processing in the presence of noise, are identified first. When comparing their performance and communication overhead, decision fusion is found to become superior to value fusion as the ratio of faulty sensors to fault free sensors increases. The use of the generalized approach to signal processing in the presence of noise under designing value and decision fusion algorithms in wireless sensor networks allows us to obtain the same performance, but at low values of signal energy, as under the employment of universally adopted signal processing algorithms widely used in practice.

• The Journal of Korean Medicine
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• v.28 no.1 s.69
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• pp.72-86
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• 2007

Objectives : The purpose of this study was to evaluate and compare the cost-effectiveness of Eastern, Western, and collaborative treatments and suggest a cost-effective approach for patients with frozen shoulder pain. Methods : Using the data of fifty-two patients, treatment effectiveness was measured by CSA, SPABI, and ROM scales and changes from the baseline score were evaluated. Data source for cost estimation was based on the national health insurance (NHI) payment system. Because the price in NHI was differentiated by health care institutions, five collaborative types were considered in assessing costs. Cost-effective ratios were computed for economic evaluation. Results : Compared with Eastern treatment, collaborative and Western treatments showed better effects on CSA scale after 4 weeks' treatment. The collaborative approach was also the most effective treatment on SPADI and ROM scales. The direct cost per patient receiving Eastern treatment was less than other treatments. In general, collaborative treatment dominated Eastern and Western treatments in cost-effectiveness an analysis. However, the cost-effectiveness ratio of Eastern treatment resulted in \9,000 compared to \29,000 of collaborative treatment on SPADI. Four different indicators of ROM scales resulted in different approaches as the cost-effective treatment. Conclusions : Considering cost-effectiveness ratios, collaborative treatment was the best treatment on CSh and SPADI scales after 4 weeks' treatment. As for ROM scales, the recommended alternatives were Eastern treatment for patients with abduction and adduction disabilities, Western treatment for those with flexion disability, and collaborative approach fir those with extension disabiliry.

• Journal of the Korean Data and Information Science Society
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• v.20 no.5
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• pp.809-818
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• 2009

Collaborative filtering approach predicts the preference of active user about specific items transacted on the e-commerce by using others' preference information. To improve the prediction accuracy through collaborative filtering approach, it must be needed to gain enough preference information of users' for predicting preference. But, a bit much information of users' preference might wrongly affect on prediction accuracy, and also too small information of users' preference might make bad effect on the prediction accuracy. This research suggests the method, which decides suitable numbers of neighbor users for applying collaborative filtering algorithm, improved by existing k nearest neighbors selection methods. The result of this research provides useful methods for improving the prediction accuracy and also refines exploratory data analysis approach for deciding appropriate numbers of nearest neighbors.

• Journal of Engineering Education Research
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• v.15 no.5
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• pp.47-53
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• 2012

Practical problem-solving studies in a company or organization have provided great advantages for our university and students. For example, such studies can lead them to build a stronger relationship with local governments and companies as well as develop their research through collaborative studies. On the other hand, comments from companies or organizations that accepted our students showed that they did not always have advantages. This study seeks ways to establish a sustainable long-term internship program that can offer advantages for companies. Advantages and disadvantages of the internship are written by the company on the evaluated sheet. These feedback comments are analyzed by text-mining approach. It is shown that there are three types of company and organizations depending on their reasons for accepting students. Next, suitable internship programs for each type, including their period and expense distribution are presented.

• Journal of Computational Design and Engineering
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• v.2 no.2
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• pp.73-78
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• 2015

In this paper, we propose a new approach to providing interactive and collaborative lens among multi-users for supporting level-of-detailed views using smartphones in a public display. In order to provide smartphone-based lens capability, the locations of smartphones are effectively detected and tracked using Kinect, which provides RGB data and depth data (RGB-D). In particular, human skeleton information is extracted from the Kinect 3D depth data to calculate the smartphone location more efficiently and correctly with respect to the public display and to support head tracking for easy target selection and adaptive view generation. The suggested interactive and collaborative lens using smartphones not only can explore local spaces of the shared display but also can provide various kinds of activities such as LOD viewing and collaborative interaction. Implementation results are given to show the advantage and effectiveness of the proposed approach.

• Industrial Engineering and Management Systems
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• v.7 no.3
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• pp.266-287
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• 2008

To manage collaborative negotiation in software system design, we have built a socio-technical argument-based negotiation management approach by integrating a Socio-technical Co-construction Process (STCP) with an Argument-based Negotiation Process (ABNP). This paper reviews relevant research work and presents each step of this approach. The STCP provides rich contextual information of technical decisions and social interactions in a system design process. The ABNP provides STCP with a negotiation management and conflict resolution strategy by guiding software engineers to generate, exchange and evaluate their argument claims in negotiation activities. In addition, this paper describes a prototype system which implements this new approach using the advanced Web-based software technologies with the goal of demonstrating how to systematically enhance the negotiation management capabilities in a dynamic socio-technical framework.

• Management Science and Financial Engineering
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• v.12 no.2
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• pp.71-85
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• 2006

Collaborative filtering, among other recommender systems, has been known as the most successful recommendation technique. However, it requires the user-item rating data, which may not be easily available. As an alternative, some collaborative filtering algorithms have been developed recently by utilizing the market basket data in the form of the binary user-item matrix. Viewing the recommendation scheme as a two-class classification problem, we proposed a new collaborative filtering scheme using a regularized discriminant analysis applied to the binary user-item data. The proposed discriminant model was built in terms of the major principal components and was used for predicting the probability of purchasing a particular item by an active user. The proposed scheme was illustrated with two modified real data sets and its performance was compared with the existing user-based approach in terms of the recommendation precision.

• The KIPS Transactions:PartB
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• v.19B no.1
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• pp.31-36
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• 2012

In recent years, there has been increasing interest in recommender systems which provide users with personalized suggestions for products or services. In particular, researches of collaborative filtering analyzing relations between users and items has become more active because of the Netflix Prize competition. This paper presents the reinforcement learning approach for collaborative filtering. By applying reinforcement learning techniques to the movie rating, we discovered the connection between a time sequence of past ratings and current ratings. For this, we first formulated the collaborative filtering problem as a Markov Decision Process. And then we trained the learning model which reflects the connection between the time sequence of past ratings and current ratings using Q-learning. The experimental results indicate that there is a significant effect on current ratings by the time sequence of past ratings.