• 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.

• The Korean Journal of Applied Statistics
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• v.26 no.5
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• pp.835-845
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• 2013

Travel-time is considered the most typical and preferred traffic information for intelligent transportation systems(ITS). This paper proposes a real-time travel-time prediction method for a national highway. In this paper, the K-nearest neighbor(KNN) method is used for travel time prediction. The KNN method (a nonparametric method) is appropriate for a real-time traffic management system because the method needs no additional assumptions or parameter calibration. The performances of various models are compared based on mean absolute percentage error(MAPE) and coefficient of variation(CV). In real application, the analysis of real traffic data collected from Korean national highways indicates that the proposed model outperforms other prediction models such as the historical average model and the Kalman filter model. It is expected to improve travel-time reliability by flexibly using travel-time from the proposed model with travel-time from the interval detectors.

• Proceedings of the Korea Information Processing Society Conference
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• 2008.11a
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• pp.714-717
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• 2008

협업여과 추천기법에는 사용자 기반 협업여과와 아이템 기반 협업여과가 있으며, 절차는 유사도 측정, 이웃 선정, 예측값 생성 단계로 이루어진다. 유사도 측정 단계에는 유클리드 거리(Euclidean Distance), 코사인 유사도(Cosine Similarity), 피어슨 상관계수(Pearson Correlation Coefficient) 방법 등이 있고, 이웃 선정 단계에는 상관 한계치(Correlation-Threshold), 근접 N 이웃(Best-N-Neighbors) 방법 등이 있다. 마지막으로 예측값 생성 단계에는 단순평균(Simple Average), 가중합(Weighted Sum), 조정 가중합(Adjusted Weighted Sum) 등이 있다. 이처럼 협업여과 추천기법에는 다양한 기법들이 사용되고 있다. 따라서 본 논문에서는 사용자 기반 협업여과와 아이템 기반 협업여과 추천기법에 사용되는 유사도 측정 기법과 예측값 생성 기법의 최적화된 조합을 알아보기 위해 성능 실험 및 비교 분석을 하였다. 실험은 GroupLens의 MovieLens 데이터 셋을 활용하였고 MAE(Mean Absolute Error)값을 이용하여 추천기법을 비교 하였다. 실험을 통해 유사도 측정 기법과 예측값 생성 기법의 최적화된 조합을 찾을 수 있었고, 사용자 기반 협업여과와 아이템 기반 협업여과의 성능비교를 통해 아이템 기반 협업여과의 성능이 보다 우수했음을 확인 하였다.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.11 no.2
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• pp.181-188
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• 2018

In this paper, we propose an analysis scheme of customer spending pattern using text mining. In proposed consumption pattern analysis scheme, first we analyze user's rating similarity using Pearson correlation, second we analyze user's review similarity using TF-IDF cosine similarity, third we analyze the consistency of the rating and review using Sendiwordnet. And we select the nearest neighbors using rating similarity and review similarity, and provide the recommended list that is proper with consumption pattern. The precision of recommended list are 0.79 for the Pearson correlation, 0.73 for the TF-IDF, and 0.82 for the proposed consumption pattern. That is, the proposed consumption pattern analysis scheme can more accurately analyze consumption pattern because it uses both quantitative rating and qualitative reviews of consumers.

• Proceedings of the Korean Information Science Society Conference
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• 2008.06a
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• pp.164-165
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• 2008

전장에서 임무 수행중인 병력이나 탱크 등을 지원하거나 보호 동물의 활동을 모니터링 하는 센서 네트워크에서는 전송 정보뿐만 아니라 그러한 대상들의 위치를 악의적 추적자로부터 보호할 수 있어야 한다. 본 논문에서는 활동 소오스 노드처럼 메시지 전송은 진행하고 있지 않지만 위치가 보호되어야 할 대상과 근접한 휴면(dormant) 소오스 노드들을 고려한 소오스 위치 보호 라우팅 기법 GSLP(GPSR-based Source-Location Privacy)를 제안한다. GSLP는 알고리즘의 간결성과 신장성(scalability)이 뛰어난 GPSR(greedy perimeter stateless routing)을 확장하여 메시지 전달 노드를 선정할 때 일정 확률로 임의의 이웃 노드를 선택하는 한편, perimeter 라우팅을 적용하여 소오스 노드들을 우회하도록 하여 위치를 보호하도록 하였다. 시뮬레이션 결과, 기존의 대표적인 소오스 위치 보호 라우팅 프로토콜인 PR-SP(Phantom Routing-Single Path)에 비해 GSLP는 휴면 소오스 노드들의 수에 거의 관계없이 높은 안전 기간(전송 메시지 수)을 일정하게 제공하면서도 전달 지연(경로의 평균 홉(hop) 수)은 도착지와의 최단 홉 수의 약 두 배 이내에 머물러 대규모 센서 네트워크에서의 소오스의 위치를 보호하기 위한 방안으로 적합한 것으로 평가되었다.

• Journal of KIISE:Information Networking
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• v.36 no.1
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• pp.12-23
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• 2009

Sensor networks deployed in battlefields to support military operations or deployed in natural habitats to monitor the rare wildlifes must take account of protection of the location of valuable assets(i.e., soldiers or wildlifes) from an adversary or malicious tracing as well as the security of messages in transit. In this paper we propose a routing protocol GSLP(GPSR-based Source-Location Privacy) that is capable of enhancing the location privacy of an active source node(i.e., message-originating node) in the presence of multiple dormant sources(i.e., nodes lying nearby an asset whose location needs to be secured). Extended is a simple, yet scalable, routing scheme GPSR(greedy perimeter stateless routing) to select randomly a next-hop node with a certain probability for randomizing paths and to perform perimeter routing for detouring dormant sources so that the privacy strength of the active source, defined as safety period, keeps enhanced. The simulation results obtained by increasing the number of dormant sources up to 1.0% of the total number of nodes show that GSLP yields increased and nearly invariant safety periods, while those of PR-SP(Phantom Routing, Single Path), a notable existing protocol for source-location privacy, rapidly drop off as the number of dormant sources increases. It turns out that delivery latencies of GSLP are roughly less than two-fold of the shortest path length between the active source and the destination.