• Journal of the Korean Society for information Management
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• v.24 no.3
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• pp.271-297
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• 2007

Since the use of the Internet has proliferated, the availability of illegal and harmful content has been a great concern to both governments and Internet users. Among various solutions for issues related to such content, Internet content filtering technologies have been developed for enabling users to deal with harmful content. In recent years, commercial filtering has become massively popular. Many parents, teachers and even governments have chosen commercial filtering software as a feasible technical solution for protecting minors from harmful information on the Internet. The Internet content filtering software market has grown significantly. However, Internet content filtering software has led to intense debate among civil liberties groups, They deem this to be censorship and argue that Internet filtering technologies are simply unworkable because they have inherent weaknesses. They are critical of the fact that most filtering has violated free speech rights and will eventually wipe out honor and controversial, yet innocent incidences of free speech on the Internet. In this article Internet content filtering, in particular PICS/RDF-based label filtering, so-called Internet content rating system, will be explored and its advantages and drawbacks relating to end-users' autonomy and freedom of expression will be discussed.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.12 no.3
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• pp.75-81
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• 2012

With the advance of internet technology and fast growing of data volume, it become very hard to find a demanding information from the huge amount of data. Recommender system can solve the delema by helping a user to find required information. This paper proposes a reinforcement learning based hybrid recommendation system to predict user's preference. The hybrid recommendation system combines the content based filtering and collaborate filtering, and the system was tested using 2000 images. We used mean abstract error(MAE) to compare the performance of the collaborative filtering, the content based filtering, the naive hybrid filtering, and the reinforcement learning algorithm based hybrid filtering methods. The experiment result shows that the performance of the proposed hybrid filtering performance based on reinforcement learning is superior to other methods.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.9 no.5
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• pp.175-181
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• 2009

In this paper, we introduce the implementation of the security system using iris recognition. This system acquires images with infrared camera and extracts the 2D code from a infrared image which uses scale-space filtering and concavity. We examine the system by (i) extract 2D code and (ii) compare the code that stored on the server (iii) mearsure FAR and FRR using pattern matching. Experiment results show that the proposed method is very suitable.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.14 no.1
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• pp.101-107
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• 2014

This paper presents an efficient distributed recommendation system using clustering collaborative filtering algorithm in distributed computing environments. The system was built based on Hadoop distributed computing platform, where distributed Min-hash clustering algorithm is combined with user based collaborative filtering algorithm to optimize recommendation performance. Experiments using Movie Lens benchmark data show that the proposed system can reduce the execution time for recommendation compare to sequential system.

• Asia pacific journal of information systems
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• v.15 no.1
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• pp.63-79
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• 2005

Recommender systems are a personalized information filtering technology to help customers find the products they would like to purchase. Collaborative filtering is known to be the most successful recommendation technology, but its widespread use has exposed some problems such as sparsity and scalability in the e-business environment. In this paper, we propose a recommendation system, WebCF-PT based on Web usage mining and product taxonomy to enhance the recommendation quality and the system performance of traditional CF-based recommender systems. Web usage mining populates the rating database by tracking customers' shopping behaviors on the Web, so leading to better quality recommendations. The product taxonomy is used to improve the performance of searching for nearest neighbors through dimensionality reduction of the rating database. A prototype recommendation system, WebCF-PT is developed and Internet shopping mall, EBIB(e-Business & Intelligence Business) is constructed to test the WebCF-PT system.

• Proceedings of the Korean Society for Information Management Conference
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• 1997.08a
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• pp.37-53
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• 1997

The huge size of Internet does not allow ordinary information seekers to search information with ease. Now, it is almost impossible to navigate the ocean of information without effective search tools. Web search engine has been the most effective technology for information retrieval on WWW. But recently, the need for new search tools on WWW or Internet has increased drastically. Currently, there are many on-going researches on the related topics. In this survey, we categorize the new search tools into four types: monitoring systems, filtering systems, browsing assistant systems, recommending systems. These example systems are examined. We are especially interested in WWW information filtering. It is studied how to apply the information filtering techniques to WWW, The application is not so straightforward like Email, Newswire filtering systems. As a result of this study, a simple WWW information filtering system is proposed.

• Proceedings of the IEEK Conference
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• 2003.11b
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• pp.77-80
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• 2003

Complying with highly demand of information through internet. the utility of computer and network is rapidly provided with to schools. This situation brings about many problems. For example, the stolen information through false identification(Hacking) is the most greatest concern. In this paper it tells that the efficient way of preservating computer use is by using operating system of Open Source, which is Linux system. Further more, it shows the system which was organized by IP-Tabling (offered service-Packet Filtering method from the Linux system) functions well as a security system.

• Journal of Internet Computing and Services
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• v.19 no.3
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• pp.7-14
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• 2018

As SNS(Social Network Service) becomes a part of our life, new information can be derived through various information provided by SNS. Through the public timeline analysis of SNS, we can extract the latest tour trends for the public and the intimacy through the social relationship analysis in the SNS. The extracted intimacy can also be used to make the personalized recommendation by adding the weights to friends with high intimacy. We apply SNS elements such as analyzed latest trends and intimacy to item-based collaborative filtering techniques to achieve better accuracy and satisfaction than existing travel recommendation services in a new way. In this paper, we propose a social travel recommendation system using item - based collaborative filtering.

• Journal of Korea Society of Industrial Information Systems
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• v.7 no.5
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• pp.21-28
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• 2002

With the proliferation of internet and an increase in internet users, several kinds of vast information are provided to users on the internet. It is increasing in the need of personalization service by filtering user preferred news among various news documents provided through several news servers.. In this paper, we implemented a filtering agent system to meet to demand for personalized news service. In the proposed system, Kohonen network is used to train keywords provided by users and to classify news groups. Resulting from that, the personalized new service is achieved. After we trained and tested the filtering agent, we could provide users news groups with their intention.

• Journal of Information Processing Systems
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• v.15 no.3
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• pp.616-631
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• 2019

With the rapid increase of information on the World Wide Web, finding useful information on the internet has become a major problem. The recommendation system helps users make decisions in complex data areas where the amount of data available is large. There are many methods that have been proposed in the recommender system. Collaborative filtering is a popular method widely used in the recommendation system. However, collaborative filtering methods still have some problems, namely cold-start problem. In this paper, we propose a movie recommendation system by using social network analysis and collaborative filtering to solve this problem associated with collaborative filtering methods. We applied personal propensity of users such as age, gender, and occupation to make relationship matrix between users, and the relationship matrix is applied to cluster user by using community detection based on edge betweenness centrality. Then the recommended system will suggest movies which were previously interested by users in the group to new users. We show shown that the proposed method is a very efficient method using mean absolute error.