• Journal of Information Technology Applications and Management
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• v.16 no.1
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• pp.97-113
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• 2009

Collaborative filtering(CF) has been a very successful approach for building recommender system, but its widespread use has exposed to some well-known problems including sparsity and scalability problems. In order to mitigate these problems, we propose two novel models for improving the typical CF algorithm, whose names are ISCF(Item-Selected CF) and USCF(User-Selected CF). The modified models of the conventional CF method that condense the original dataset by reducing a dimension of items or users in the user-item matrix may improve the prediction accuracy as well as the efficiency of the conventional CF algorithm. As a tool to optimize the reduction of a user-item matrix, our study proposes genetic algorithms. We believe that our approach may relieve the sparsity and scalability problems. To validate the applicability of ISCF and USCF, we applied them to the MovieLens dataset. Experimental results showed that both the efficiency and the accuracy were enhanced in our proposed models.

• Journal of Information Science Theory and Practice
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• v.7 no.3
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• pp.31-39
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• 2019

Nowadays, recommender systems suggest lists of items to users considering not only accuracy but also diversity and novelty. However, suggesting the most diverse list of items to all users is not always acceptable, since different users prefer and/or tolerate different degree of diversity. Hence suggesting a personalized list with a diversity degree considering each user preference would improve the efficiency of recommender systems. The main contribution and novelty of this study is to tune the diversity degree of the recommendation list based on the users' variety-seeking feature, which ultimately leads to users' satisfaction. The proposed approach considers the similarity of users' desire diversity as a new parameter in addition to the usual similarity of users in the state-of-the-art collaborative filtering algorithm. Experimental results show that the proposed approach improves the personal diversity criterion comparing to the closest method in the literature, without decreasing accuracy.

• Proceedings of the Korea Information Processing Society Conference
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• 2022.05a
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• pp.264-267
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• 2022

Recently, federated learning (FL) has increased prominence as a viable approach for enhancing user privacy and data security by allowing collaborative multi-party model learning without exchanging sensitive data. Despite this, most present FL systems still depend on a centralized aggregator to generate a global model by gathering all submitted models from users, which could expose user privacy and the risk of various threats from malicious users. To solve these issues, we suggested a safe FL framework that employs differential privacy to counter membership inference attacks during the collaborative FL model training process and empowers blockchain to replace the centralized aggregator server.

• The Journal of Information Systems
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• v.27 no.2
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• pp.197-219
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• 2018

Purpose The purpose of this study is to examine what motivates users to adopt one of the emerging applications for collaborative consumption of sharing economy. Using the self-determination theory, motivation theory and TAM(Technology Acceptance Model) as the theoretical framework, this study illustrates important factors that influence adoption of collaborative consumption service. We develops the ICTs(Information and Communications Technologies) initiatives and motivation model to collaborative consumption. Design/methodology/approach This paper makes use of a quantitative methodology using survey questionnaire that allows for the measurement of the eight constructs(System Availability, Contents Quality, Design & Personalization, Security & Privacy, Emotional & Social Value, Economic Value, Attitude, Adoption & Consumption) contained in the hypothesized theoretical model on the basis of the prior literatures. Data collected from a sample of 227 respondents who have used the collaborative consumption services and provided the foundation for the examination of the proposed relationships in the model. Findings This study has the following implications for the users and providers of CC platforms and services. The ICTs initiatives (System Availability, Contents Quality, Design & Personalization, Security & Privacy) are the influential factors that motivate the emotional and social value to CC. On the other hand, The ICTs initiatives (System Availability, Contents Quality) are not very significant factors of economic value to CC. The empirical analysis result indicate that there are significant causal effect among emotional & social value, economic value, and adoption to CC. This study provides important theoretical implications for innovation adoption research through an empirical examination of the relationship between ICTs initiatives, motivation factors to collaborative consumption in the sharing economy.

• Journal of Intelligence and Information Systems
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• v.14 no.1
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• pp.101-115
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• 2008

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.

• Asia pacific journal of information systems
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• v.15 no.4
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• pp.135-152
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• 2005

As the most widely used media of BtoB e-business, the e-Marketplace can be a way of BtoB e-buisness continuously in the age of c-Commerce with the functional collaborative integration between the e-Marketplace and corporate systems. Moreover, collaborative operations like QR, VMI and so on have shown a great efficiency in the area of BtoB supply chain management. However, some critical considerations are discussed in the selection of trade partners between the e-Marketplace and the SCM. In e-Marketplaces, the intermediation to select partnersusually focuses on the competitive process for lower price. However, in the SCM, the relationship with strategic alliance is more importantly addressed for efficiency. Considering the trend to c-Commerce in Internet commerce, the approach to the collaborative relationship in BtoB commerce has important meanings. In this paper, we proposed and appraised an architecture where the e-Marketplace can be an elelctronic functional method for the relationship based BtoB e-business from the viewpoint of SCM and c-Commerce.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2006.05a
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• pp.619-620
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• 2006

In this paper, the CAE Service System for Collaborative Engineering Environment with web services and Multi-frontal Method has been investigated and developed. The enabling technologies such as SOAP and .NET Framework play great roles in the development of integrated distributed application software. In addition to the distribution of analysis modules, numerical solution process itself is again divided into parallel processes using Multi-frontal Method for computational efficiency. We believe that the proposed approach for the analysis can be extended to the entire product development process for sharing and utilizing common product data in the distributed engineering environment.

• The Journal of Society for e-Business Studies
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• v.20 no.3
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• pp.79-87
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• 2015

With recent emergence of Web 2.0 technology, many services are encouraging the user participation. Then, many approaches dealing with multimedia contents focused on the personalized information provisioning. The proposed approach analyzes the user requirements and previous methodology for personalized information provisioning. Furthermore, we propose the user participation based multimedia services with collaborative tagging.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2003.05a
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• pp.400-409
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• 2003

Collaborative filtering (CF) recommendation is a knowledge sharing technology for distribution of opinions and facilitating contacts in network society between people with similar interests. The main concerns of the CF algorithm are about prediction accuracy, speed of response time, problem of data sparsity, and scalability. In general, the efforts of improving prediction algorithms and lessening response time are decoupled. We propose a three-step CF recommendation model which is composed of profiling, inferring, and predicting steps while considering prediction accuracy and computing speed simultaneously. This model combines a CF algorithm with two machine learning processes, SOM (Self-Organizing Map) and CBR (Case Based Reasoning) by changing an unsupervised clustering problem into a supervised user preference reasoning problem, which is a novel approach for the CF recommendation field. This paper demonstrates the utility of the CF recommendation based on SOM cluster-indexing CBR with validation against control algorithms through an open dataset of user preference.

• Journal of Intelligence and Information Systems
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• v.9 no.3
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• pp.117-135
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• 2003

This study is to propose a procedure solving scale problem of traditional collaborative filtering (CF) approach. The CF approach generally uses some similarity measures like correlation coefficient. So, as the user of the Website increases, the complexity of computation increases exponentially. To solve the scale problem, this study suggests a clustering model-based approach using Self-Organizing Map (SOM) and RFM (Recency, Frequency, Momentary) method. SOM clusters users into some user groups. The preference score of each item in a group is computed using RFM method. The items are sorted and stored in their preference score order. If an active user logins in the system, SOM determines a user group according to the user's characteristics. And the system recommends items to the user using the stored information for the group. If the user evaluates the recommended items, the system determines whether it will be updated or not. Experimental results applied to MovieLens dataset show that the proposed method outperforms than the traditional CF method comparatively in the recommendation performance and the computation complexity.