• The Journal of Bigdata
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• v.2 no.1
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• pp.41-48
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• 2017

Recently, the popularity of the recommendation system and the evaluation of the performance of the algorithm of the recommendation system have become important. In this study, we used modeling and RMSE to verify the effectiveness of various algorithms in movie data. The data of this study is based on user-based collaborative filtering using Pearson correlation coefficient, item-based collaborative filtering using cosine correlation coefficient, and item-based collaborative filtering model using singular value decomposition. As a result of evaluating the scores with three recommendation models, we found that item-based collaborative filtering accuracy is much higher than user-based collaborative filtering, and it is found that matrix recommendation is better when using matrix decomposition.

• The Journal of Korean Association of Computer Education
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• v.6 no.2
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• pp.147-156
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• 2003

Collaborative Filtering is a popular technology in electronic commerce, which adapt the opinions of entire communities to provide interesting products or personalized resources and items. It has been applied to many kinds of electronic commerce domain since Collaborative Filtering has proven an accurate and reliable tool. But educational application remain limited yet. We design collaborative filtering recommendation system using user's ratings in educational contents recommendation. Also We propose a method of similarity compensation using user's information for improvement of recommendation accuracy. The proposed method is more efficient than the traditional collaborative filtering method by experimental comparisons of mean absolute error(MAE) and reciever operating characteristics(ROC) values.

• 한국HCI학회:학술대회논문집
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• 2007.02b
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• pp.472-477
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• 2007

사용자 중심의 디자인 패러다임이 도래하면서 점차 사용성 진단이 제품 혹은 서비스의 전체 개발 프로세스 상에서 필수적인 절차이며 이를 통해 사용자의 경험 품질 향상을 모색할 수 있다는 인식이 확산되고 있다. 실제로도 제품 개발 과정의 한 꼭지로서 사용자 리서치를 수행하는 기업이 점차 증가되고 있지만, 리서치를 통해 도출된 결과가 실제 제품 개발에 얼마나 효과적으로 반영되는지에 대해서는 의문을 가지게 된다. 이러한 의문을 품게 된 이유는 사용자 리서치 결과가 실제 제품이나 서비스에 적용된 정도가 크지 않은 경우를 종종 목격하였기 때문이다. 이러한 주요 원인 중 하나로서 리서치 결과가 실무 담당자들에게 전달되는 과정에서 발생될 수 있는 해석 상의 차이 때문일 것이라는 가설을 가지고 사용성 진단 리서치 프로세스의 변화를 시도해 보았다. 다시 말하면, 실제 현장에서 단기간 동안 진행되는 사용자 리서치를 통해 보다 효과적으로 그 결과를 활용할 수 있는 방안으로 리서치 진행자와 실무 담당자간 협업 프로세스 작업 단계인 협업 디자인 워크샵(co-designing workshop)을 제안하였다. 본 연구는 협업 디자인 워크샵 프로세스의 소개를 목적으로 하였으며, 본 연구의 결과가 사용성 진단 리서치 결과의 활용도 및 가치를 향상시키고, 사용자의 입장을 대변하는 리서처의 책임과 역할을 충실히 수행하는데 도움이 될 수 있기를 기대한다.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.34 no.8
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• pp.953-964
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• 2010

In this study, we proposed duty executions to confine a robot in a specific place with multiple robots. The proposed method involved the use of a role classifier for assigning labor roles, behavior selector for each robot, and a collaboration manager for handling complex situations. Further, we verified the validity of the proposed method by performing simulations to confine a robot in the specific location by using multiple robots.

• Proceedings of the Korean Society of Computer Information Conference
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• 2015.07a
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• pp.169-172
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• 2015

웹툰의 양은 방대하여 사용자가 원하는 웹툰을 찾는데 어려움이 있기 때문에 체계적인 추천 시스템이 필요하다. 하지만 기존의 추천 시스템은 조회수가 많은 인기 웹툰을 추천하는 방식과 사용자와 비슷한 연령대, 성별의 사용자들이 조회한 콘텐츠를 추천해주는 인구 통계학적 추천(demographic filtering)방식, 그리고 비슷한 사용자를 분석하여 추천해주는 협업적 추천(collaborative filtering)방식에 국한되어 있어, 개인의 성향을 반영하여 추천하고 있다고 보기 어렵다. 따라서 사용자 개인의 성향을 분석하는 방식에 대한 시도가 필요하다. 본 연구에서는 이러한 한계를 극복하기 위해서 개인의 성향을 분석하는 지표로 MBTI(Myers-Briggs Type Indicator) 유형을 이용하고, 같은 MBTI 유형의 사용자간의 협업적 필터링 추천 방식을 제안하였다. 또, 협업적 필터링 방식에서 발생하는 콜드 스타트 문제와 초기 평가자 문제를 해결하는 방안을 제시하였다.

• KIPS Transactions on Computer and Communication Systems
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• v.2 no.11
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• pp.475-482
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• 2013

Several approaches to recommendation systems have been studied. One of the most successful technologies for building personalization and recommendation systems is collaborative filtering, which is a technique that provides a process of filtering customer information based on such information profiles. Collaborative filtering systems, however, have a sparsity if there is not enough data to recommend. In this paper, we suggest a movie recommendation system, based on the weighted personal propensity and the collaborating filtering system, in order to provide a solution to such sparsity. Furthermore, we assess the system's applicability by using the open database MovieLens, and present a weighted personal propensity framework for improvement in the performance of recommender systems. We successfully come up with a movie recommendation system through the optimal personalization factors.

• Proceedings of the Korea Information Processing Society Conference
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• 2002.04b
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• pp.1093-1096
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• 2002

기업간 비즈니스 거래를 하기 위해서는 B2B(Business to Business)기업이 지원하는 업무 프로세스에 관한 정보와 업무 정보 교환을 위해 기업의 기술적인 사항을 정의하고 거래 기업간에 비즈니스 거래를 위하여 거래 파트너간 합의된 상호작용을 정의한 문서가 필요하다. 이러한 문서를 XML 기반의 개방형 전자상거래 프레임 워크인 ebXML(electronic business XML)에서는 기업의 비즈니스 협업능력을 정의한 전자를 협업 프로토콜 프로파일(Collaboration-Protocol Profile:CPP)이라 하고 거래 기업간에 비즈니스 협업 상호작용을 정의한 후자를 협업 프로토콜 약정서(Collaboration-Protocol Agreement:CPA)라고 한다. 본 논문에서는 ebXML에서 거래 기업간 상호 운용성을 증대시키는 CPP문서를 효율적으로 저작 할 수 있는 생성기와 거래 기업들의 CPP 문서들을 기본으로 상호 협업을 정의한 CPA 문서를 저작 할 수 있는 Composer 시스템을 설계 및 구현하였다.

• Journal of Internet Computing and Services
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• v.10 no.6
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• pp.81-98
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• 2009

Collaborative filtering, one of the most successful technologies among recommender systems, is a system assisting users in easily finding the useful information and supporting the decision making. However, despite of its success and popularity, one notable issue is incredibility of recommendations by unreliable users called shilling attacks. To deal with this problem, in this paper, we analyze the type of shilling attacks and propose a unique method of building a model for protecting the recommender system against manipulated ratings. In addition, we present a method of applying the model to collaborative filtering which is highly robust and stable to shilling attacks.

• The Journal of Korean Institute of Information Technology
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• v.16 no.12
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• pp.109-114
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• 2018

Collaborative filtering has been widely utilized in recommender systems as typical algorithm for outstanding performance. Since it depends on item rating history structurally, The more sparse rating matrix is, the lower its recommendation accuracy is, and sometimes it is totally useless. Variety of hybrid approaches have tried to combine collaborative filtering and content-based method for improving the sparsity issue in rating matrix. In this study, a new method is suggested for the same purpose, but with different perspective, it deals with no-match situation in person-person similarity evaluation. This method is called the transitive similarity model because it is based on relation graph of people, and it compares recommendation accuracy by applying to Movielens open dataset.

• Journal of Intelligence and Information Systems
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• v.20 no.4
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• pp.107-120
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• 2014

This study proposes a novel recommender system using the structural hole analysis to reflect qualitative and emotional information in recommendation process. Although collaborative filtering (CF) is known as the most popular recommendation algorithm, it has some limitations including scalability and sparsity problems. The scalability problem arises when the volume of users and items become quite large. It means that CF cannot scale up due to large computation time for finding neighbors from the user-item matrix as the number of users and items increases in real-world e-commerce sites. Sparsity is a common problem of most recommender systems due to the fact that users generally evaluate only a small portion of the whole items. In addition, the cold-start problem is the special case of the sparsity problem when users or items newly added to the system with no ratings at all. When the user's preference evaluation data is sparse, two users or items are unlikely to have common ratings, and finally, CF will predict ratings using a very limited number of similar users. Moreover, it may produces biased recommendations because similarity weights may be estimated using only a small portion of rating data. In this study, we suggest a novel limitation of the conventional CF. The limitation is that CF does not consider qualitative and emotional information about users in the recommendation process because it only utilizes user's preference scores of the user-item matrix. To address this novel limitation, this study proposes cluster-indexing CF model with the structural hole analysis for recommendations. In general, the structural hole means a location which connects two separate actors without any redundant connections in the network. The actor who occupies the structural hole can easily access to non-redundant, various and fresh information. Therefore, the actor who occupies the structural hole may be a important person in the focal network and he or she may be the representative person in the focal subgroup in the network. Thus, his or her characteristics may represent the general characteristics of the users in the focal subgroup. In this sense, we can distinguish friends and strangers of the focal user utilizing the structural hole analysis. This study uses the structural hole analysis to select structural holes in subgroups as an initial seeds for a cluster analysis. First, we gather data about users' preference ratings for items and their social network information. For gathering research data, we develop a data collection system. Then, we perform structural hole analysis and find structural holes of social network. Next, we use these structural holes as cluster centroids for the clustering algorithm. Finally, this study makes recommendations using CF within user's cluster, and compare the recommendation performances of comparative models. For implementing experiments of the proposed model, we composite the experimental results from two experiments. The first experiment is the structural hole analysis. For the first one, this study employs a software package for the analysis of social network data - UCINET version 6. The second one is for performing modified clustering, and CF using the result of the cluster analysis. We develop an experimental system using VBA (Visual Basic for Application) of Microsoft Excel 2007 for the second one. This study designs to analyzing clustering based on a novel similarity measure - Pearson correlation between user preference rating vectors for the modified clustering experiment. In addition, this study uses 'all-but-one' approach for the CF experiment. In order to validate the effectiveness of our proposed model, we apply three comparative types of CF models to the same dataset. The experimental results show that the proposed model outperforms the other comparative models. In especial, the proposed model significantly performs better than two comparative modes with the cluster analysis from the statistical significance test. However, the difference between the proposed model and the naive model does not have statistical significance.