• Journal of Korean Society of Industrial and Systems Engineering
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• v.36 no.1
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• pp.64-69
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• 2013

In ubiquitous computing, shared environments adjust themselves so that all users in the environments are satisfied as possible. Inevitably, some of users sacrifice their satisfactions while the shared environments maximize the sum of all users' satisfactions. In our previous work, we have proposed social welfare functions to avoid a situation which some users in the system face the worst setting of environments. In this work, we consider a more direct approach which is a preference based clustering to handle this issue. In this approach, first, we categorize all users into several subgroups in which users have similar tastes to environmental parameters based on their preference information. Second, we assign the subgroups into different time or space of the shared environments. Finally, each shared environments can be adjusted to maximize satisfactions of each subgroup and consequently the optimal of overall system can be achieved. We demonstrate the effectiveness of our approach with a numerical analysis.

• Nuclear Engineering and Technology
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• v.42 no.4
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• pp.414-425
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• 2010

In this paper, a procedure is developed for identifying a number of representative solutions manageable for decision-making in a multiobjective optimization problem concerning the test intervals of the components of a safety system of a nuclear power plant. Pareto Front solutions are identified by a genetic algorithm and then clustered by subtractive clustering into "families". On the basis of the decision maker's preferences, each family is then synthetically represented by a "head of the family" solution. This is done by introducing a scoring system that ranks the solutions with respect to the different objectives: a fuzzy preference assignment is employed to this purpose. Level Diagrams are then used to represent, analyze and interpret the Pareto Fronts reduced to the head-of-the-family solutions.

• Journal of Information Technology Applications and Management
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• v.20 no.3_spc
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• pp.219-230
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• 2013

Real time accessiblity and agility in Ubiquitous-commerce is required under ubiquitous computing environment. The Research has been actively processed in e-commerce so as to improve the accuracy of recommendation. Existing Collaborative filtering (CF) can not reflect contents of the items and has the problem of the process of selection in the neighborhood user group and the problems of sparsity and scalability as well. Although a system has been practically used to improve these defects, it still does not reflect attributes of the item. In this paper, to solve this problem, We can use a implicit method which is used by customer's data and purchase history data. We propose a new clustering method of weighted preference for customer using k-means clustering and Bayesian network in order to improve the accuracy of recommendation. To verify improved performance of the proposed system, we make experiments with dataset collected in a cosmetic internet shopping mall.

• Food Science and Biotechnology
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• v.17 no.3
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• pp.495-500
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• 2008

This study assessed the role of fatty acids on beef preference of 2 consumer groups from South Korea and Australia. Three muscles (longissimus dorsi, triceps brachii, and semimembranosus) were obtained from 36 carcasses (18 Hanwoo steers and 18 Angus steers) and the cooked beef samples were evaluated by 1,080 consumers (720 Korean consumer panels and 360 Australian consumer panels). The cluster analysis showed that the Korean consumers had more significant relationship with fatty acid composition of beef than that of the Australian consumers when evaluated Australian Angus beef Only C20:5(n-3), and C22:5(n-3) affected preference clustering for Australian consumers; while saturated (C16:0 and C 18:0) as well as unsaturated fatty acids [C16:1(n-7), C18:2(n-6), C18:3(n-3), C20:3(n-6), C20:4(n-6), C20:5(n-3), C22:4(n-6), C22:5(n-3)] affected preference clustering for Korean consumers (p<0.05). In the discriminant analysis of Korean consumer's preference clustering, C20:5(n-3) was a significant fatty acid for Australian Angus beef while the C20:4(n-6) and C 18:0 for Korean Hanwoo beef to evaluate the palatability (p<0.05). Therefore, fatty acid compositions impact Korean consumer's preference of beef.

• The Korean Journal of Applied Statistics
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• v.35 no.2
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• pp.265-283
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• 2022

MovieLens data consists of recorded movie evaluations that was often used to measure the evaluation score in the recommendation system research field. In this paper, we provide additional information obtained by clustering user-specific genre preference information through movie evaluation data and movie genre data. Because the number of movie ratings per user is very low compared to the total number of movies, the missing rate in this data is very high. For this reason, there are limitations in applying the existing clustering methods. In this paper, we propose a convex clustering-based method using the pairwise fused penalty motivated by the analysis of MovieLens data. In particular, the proposed clustering method execute missing imputation, and at the same time uses movie evaluation and genre weights for each movie to cluster genre preference information possessed by each individual. We compute the proposed optimization using alternating direction method of multipliers algorithm. It is shown that the proposed clustering method is less sensitive to noise and outliers than the existing method through simulation and MovieLens data application.

• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.4
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• pp.443-449
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• 2014

Preferences of users on movies are observables of various factors that are related with user attributes and movie features. For movie recommendation, analysis methods for relation among users, movies, and preference patterns are mandatory. As a relational analysis tool, we focus on the Infinite Relational Model (IRM) which was introduced as a tool for multiple concept search. We show that IRM-based co-clustering on preference patterns and movie descriptors can be used as the first tool for movie recommender methods, especially content-based filtering approaches. By introducing a set of well-defined tag sets for movies and doing three-way co-clustering on a movie-rating matrix and a movie-tag matrix, we discovered various explainable relations among users and movies. We suggest various usages of IRM-based co-clustering, espcially, for incremental and dynamic recommender systems.

• Journal of the Korea Society of Computer and Information
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• v.25 no.5
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• pp.99-106
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• 2020

The scalability problem inherent in collaborative filtering-based recommender systems has been an issue in related studies during past decades. Clustering is a well-known technique for handling this problem, but has not been actively studied due to its low performance. This paper adopts a clustering method to overcome the scalability problem, inherent drawback of collaborative filtering systems. Furthermore, in order to handle performance degradation caused by applying clustering into collaborative filtering, we take two strategies into account. First, we use fuzzy clustering and secondly, we propose and apply a similarity estimation method based on user preference for movie genres. The proposed method of this study is evaluated through experiments and compared with several previous relevant methods in terms of major performance metrics. Experimental results show that the proposed demonstrated superior performance in prediction and rank accuracies and comparable performance to the best method in our experiments in recommendation accuracy.

• Journal of Intelligence and Information Systems
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• v.21 no.1
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• pp.119-142
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• 2015

With the explosive growth in the volume of information, Internet users are experiencing considerable difficulties in obtaining necessary information online. Against this backdrop, ever-greater importance is being placed on a recommender system that provides information catered to user preferences and tastes in an attempt to address issues associated with information overload. To this end, a number of techniques have been proposed, including content-based filtering (CBF), demographic filtering (DF) and collaborative filtering (CF). Among them, CBF and DF require external information and thus cannot be applied to a variety of domains. CF, on the other hand, is widely used since it is relatively free from the domain constraint. The CF technique is broadly classified into memory-based CF, model-based CF and hybrid CF. Model-based CF addresses the drawbacks of CF by considering the Bayesian model, clustering model or dependency network model. This filtering technique not only improves the sparsity and scalability issues but also boosts predictive performance. However, it involves expensive model-building and results in a tradeoff between performance and scalability. Such tradeoff is attributed to reduced coverage, which is a type of sparsity issues. In addition, expensive model-building may lead to performance instability since changes in the domain environment cannot be immediately incorporated into the model due to high costs involved. Cumulative changes in the domain environment that have failed to be reflected eventually undermine system performance. This study incorporates the Markov model of transition probabilities and the concept of fuzzy clustering with CBCF to propose predictive clustering-based CF (PCCF) that solves the issues of reduced coverage and of unstable performance. The method improves performance instability by tracking the changes in user preferences and bridging the gap between the static model and dynamic users. Furthermore, the issue of reduced coverage also improves by expanding the coverage based on transition probabilities and clustering probabilities. The proposed method consists of four processes. First, user preferences are normalized in preference clustering. Second, changes in user preferences are detected from review score entries during preference transition detection. Third, user propensities are normalized using patterns of changes (propensities) in user preferences in propensity clustering. Lastly, the preference prediction model is developed to predict user preferences for items during preference prediction. The proposed method has been validated by testing the robustness of performance instability and scalability-performance tradeoff. The initial test compared and analyzed the performance of individual recommender systems each enabled by IBCF, CBCF, ICFEC and PCCF under an environment where data sparsity had been minimized. The following test adjusted the optimal number of clusters in CBCF, ICFEC and PCCF for a comparative analysis of subsequent changes in the system performance. The test results revealed that the suggested method produced insignificant improvement in performance in comparison with the existing techniques. In addition, it failed to achieve significant improvement in the standard deviation that indicates the degree of data fluctuation. Notwithstanding, it resulted in marked improvement over the existing techniques in terms of range that indicates the level of performance fluctuation. The level of performance fluctuation before and after the model generation improved by 51.31% in the initial test. Then in the following test, there has been 36.05% improvement in the level of performance fluctuation driven by the changes in the number of clusters. This signifies that the proposed method, despite the slight performance improvement, clearly offers better performance stability compared to the existing techniques. Further research on this study will be directed toward enhancing the recommendation performance that failed to demonstrate significant improvement over the existing techniques. The future research will consider the introduction of a high-dimensional parameter-free clustering algorithm or deep learning-based model in order to improve performance in recommendations.

• ETRI Journal
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• v.37 no.2
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• pp.317-327
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• 2015

A reliable analysis of consumer preference from a large amount of purchase data acquired in real time and an accurate customer characterization technique are essential for successful direct marketing campaigns. In this study, an optimal segmentation of post office customers in Korea is performed using a subspace projection-based clustering method to generate an accurate customer characterization from a high-dimensional census dataset. Moreover, a traditional temporal mining method is extended to an algorithm using the MapReduce framework for a consumer preference analysis. The experimental results show that it is possible to use parallel mining through a MapReduce-based algorithm and that the execution time of the algorithm is faster than that of a traditional method.

• Journal of Korea Multimedia Society
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• v.20 no.2
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• pp.336-343
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• 2017

We propose a real time personalized recommendation algorithm on the mobile device. We use a unified collaborative filtering with reduced data. We use Fuzzy C-means clustering to obtain the reduced data and Konohen SOM is applied to get initial values of the cluster centers. The proposed algorithm overcomes data sparsity since it extends data to the similar users and similar items. Also, it enables real time service on the mobile device since it reduces computing time by data clustering. Applying the suggested algorithm to the MovieLens data, we show that the suggested algorithm has reasonable performance in comparison with collaborative filtering. We developed Android-based smart-phone application, which recommends restaurants with coupons and restaurant information.