• International journal of advanced smart convergence
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• 제9권3호
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• pp.42-48
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• 2020

These days people are overwhelmed by information on the Internet thus searching for useful information becomes burdensome, often failing to acquire some in a reasonable time. Recommender systems are indispensable to fulfill such user needs through many practical commercial sites. This study proposes a novel similarity measure for user-based collaborative filtering which is a most popular technique for recommender systems. Compared to existing similarity measures, the main advantages of the suggested measure are that it takes all the ratings given by users into account for computing similarity, thus relieving the inherent data sparsity problem and that it reflects the uncertainty or vagueness of user ratings through fuzzy logic. Performance of the proposed measure is examined by conducting extensive experiments. It is found that it demonstrates superiority over previous relevant measures in terms of major quality metrics.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제6권2호
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• pp.110-115
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• 2006

In pure P2P environment, it is possible to provide service by using a little real-time information without using accumulated information. But in case of using only a little information that was locally collected, quality of recommendation service can be fallen-off. Therefore, it is necessary to study a method to improve qualify of recommendation service by using users' context information. But because a great volume of users' context information can be recognized in a moment, there can be a scalability problem and there are limitations in supporting differentiated services according to fields and items. In this paper, we solved the scalability problem by clustering context information per each service field and classifying it per each user, using SOM. In addition, we could recommend proper services for users by quantifying the context information of the users belonging to the similar classification to the service requester among classified data and then using collaborative filtering.

• Journal of Computing Science and Engineering
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• 제11권3호
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• pp.100-108
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• 2017

In the field of Internet of Things (IoT), smarter embedded devices offer functions via web services. The Quality-of-Service (QoS) prediction is a key measure that guarantees successful IoT service applications. In this study, a collaborative filtering method is presented for predicting response time of IoT service due to time-awareness characteristics of IoT. First, a calculation method of service response time similarity between different users is proposed. Then, to improve prediction accuracy, initial similarity values are adjusted and similar neighbors are selected by a similarity threshold. Finally, via a densified user-item matrix, service response time is predicted by collaborative filtering for current active users. The presented method is validated by experiments on a real web service QoS dataset. Experimental results indicate that better prediction accuracy can be achieved with the presented method.

• 한국멀티미디어학회논문지
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• 제20권2호
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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.

• 한국CDE학회논문집
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• 제5권3호
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• pp.263-275
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• 2000

Multidisciplinary design optimization(MDO) can yield optimal design considering all the disciplinary requirements concurrently. A method to implement the collaborative optimization(CO) approach, one of the MDO methodologies, is developed using a pre-compiler “EzpreCompiler”, a design optimization library “EzOptimizer”, and a common object request broker architecture(CORBA) in distributed computing environment. The CO approach is applied to a mathematical example to show its applicability and equivalence to standard optimization(SO) formulation. In a realistic engineering problem such as optimal design of a two-member hub frame, optimal design of a speed reducer and initial design of a bulk carrier, the CO yields better results than the SO. Furthermore, the CO allows the distributed processing using the CORBA, which leads to reduction of overall computation time.

• ETRI Journal
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• 제32권4호
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• pp.634-637
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• 2010

This letter presents a model for a dynamic collaboration (DC) platform among cloud providers (CPs) that prevents adverse business impacts, cloud vendor lock-in and violation of service level agreements with consumers, and also offers collaborative cloud services to consumers. We consider two major challenges. The first challenge is to find an appropriate market model in order to enable the DC platform. The second is to select suitable collaborative partners to provide services. We propose a novel combinatorial auction-based cloud market model that enables a DC platform among CPs. We also propose a new promising multi-objective optimization model to quantitatively evaluate the partners. Simulation experiments were conducted to verify both of the proposed models.

• 한국지능정보시스템학회:학술대회논문집
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• 한국지능정보시스템학회 2003년도 춘계학술대회
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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.

• 한국컴퓨터정보학회논문지
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• 제24권9호
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• pp.77-82
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• 2019

Collaborative filtering is a popular technique for recommender systems and used in many practical commercial systems. Its basic principle is select similar neighbors of a current user and from their past preference information on items the system makes recommendations for the current user. One of the major problems inherent in this type of system is data sparsity of ratings. This is mainly caused from the underlying similarity measures which produce neighbors based on the ratings records. This paper handles this problem and suggests a new similarity measure. The proposed method takes users rating patterns into account for computing similarity, without just relying on the commonly rated items as in previous measures. Performance experiments of various existing measures are conducted and their performance is compared in terms of major performance metrics. As a result, the proposed measure reveals better or comparable achievements in all the metrics considered.

• 한국정보과학회논문지:컴퓨팅의 실제 및 레터
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• 제6권2호
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• pp.206-215
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• 2000

본 논문은 원격 공동 실험을 위한 플랫폼인 PCS(Platform for Collaborative System) 개발에 관한 연구이다. PCS는 특정 응용 분야를 목적으로 하지 않고, 모든 분야의 공동 실험 시스템을 개발할 수 있는 범용의 공동 실험 플랫폼이다. PCS는 공동 실험에 필요한 객체인 통신, 세션, 사용자, 어플리케이션, 미디어, 메시지 객체와 이들에 대한 관리 메커니즘 함수를 가진 관리 객체로 구성된다. 관리 객체는 공동 실험 객체와 이들을 관리하는 오퍼레이션으로 구성되며, 관리 객체 사이의 상호작용을 통하여 제어정보 및 데이타를 처리한다. 또한, PCS는 공동 실험을 위한 어플리케이션 공유 기술을 통하여 단일 사용자 어플리케이션을 다중 사용자 어플리케이션으로 활용할 수 있도록 하고, 공유 어플리케이션을 통한 원격기기 제어 기능을 제공한다. PCS 플랫폼은 원격 공동 실험을 위한 환경을 제공하며, 여러 공동 실험 시스템의 하부 구조로 사용될 수 있다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제18권1호
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• pp.211-232
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• 2024

In this paper, we consider the resource allocation and offloading decisions of device-to-device (D2D) cooperative UAV-assisted mobile edge computing (MEC) system, where the device with task request is served by unmanned aerial vehicle (UAV) equipped with MEC server and D2D device with idle resources. On the one hand, to ensure the fairness of time-delay sensitive devices, when UAV computing resources are relatively sufficient, an optimization model is established to minimize the maximum delay of device computing tasks. The original non-convex objective problem is decomposed into two subproblems, and the suboptimal solution of the optimization problem is obtained by alternate iteration of two subproblems. On the other hand, when the device only needs to complete the task within a tolerable delay, we consider the offloading priorities of task to minimize UAV computing resources. Then we build the model of joint offloading decision and power allocation optimization. Through theoretical analysis based on KKT conditions, we elicit the relationship between the amount of computing task data and the optimal resource allocation. The simulation results show that the D2D cooperation scheme proposed in this paper is effective in reducing the completion delay of computing tasks and saving UAV computing resources.