• Journal of the Korean Institute of Intelligent Systems
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• v.23 no.1
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• pp.7-11
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• 2013

Taking experts' knowledge to recommend items has shown some promising results in recommender system research. In order to improve the performance of the existing recommendation algorithms, previous researches on expert-based recommender systems have exploited the knowledge of a common expert group for all users. In this paper, we study a problem of identifying personalized experts within a user group, assuming each user needs different kinds and levels of expert help. To demonstrate this idea, we present a framework for using Support Vector Machine (SVM) to find varying expert groups for users; it is shown in an experiment that the proposed SVM approach can identify personalized experts, and that the person-alized expert-based collaborative filtering (CF) can yield better results than k-Nearest Neighbor (kNN) algorithm.

• Journal of the Korea Convergence Society
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• v.9 no.10
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• pp.21-28
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• 2018

To help users who are experiencing difficulties finding the right learning course corresponding to their level of proficiency, we developed a recommendation model for personalized learning course for Intelligence Tutoring System(ITS). The Personalized Learning Course Recommendation model for ITS analyzes the learner profile and extracts the keyword by calculating the weight of each word. The similarity of vector between extracted words is measured through the cosine similarity method. Finally, the three courses of top similarity are recommended for learners. To analyze the effects of the recommendation model, we applied the recommendation model to the Women's ability development center. And mean, standard deviation, skewness, and kurtosis values of question items were calculated through the satisfaction survey. The results of the experiment showed high satisfaction levels in accuracy, novelty, self-reference and usefulness, which proved the effectiveness of the recommendation model. This study is meaningful in the sense that it suggested a learner-centered recommendation system based on machine learning, which has not been researched enough both in domestic, foreign domains.

• The KIPS Transactions:PartD
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• v.9D no.6
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• pp.1009-1016
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• 2002

We design and implement a new case-based recommender system using implicit rating information for a digital content site. Our system consists of the User Profile Generation module, the Similarity Evaluation and Recommendation module, and the Personalized Mailing module. In the User Profile Generation Module, we define intra-attribute and inter-attribute weight deriver from own's past interests of a user stored in the access logs to extract individual preferences for a content. A new similarity function is presented in the Similarity Evaluation and Recommendation Module to estimate similarities between new items set and the user profile. The Personalized Mailing Module sends individual recommended mails that are transformed into platform-independent XML document format to users. To verify the efficiency of our system, we have performed experimental comparisons between the proposed model and the collaborative filtering technique by mean absolute error (MAE) and receiver operating characteristic (ROC) values. The results show that the proposed model is more efficient than the traditional collaborative filtering technique.

• The Journal of the Korea Contents Association
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• v.16 no.12
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• pp.181-191
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• 2016

We analyzed the search word of the media content in the IPTV service, and as a result we found that an important factor is general meta information as well as content(material, plot, etc.) and emotion information in the media content selection criteria of customers. Therefore, in this research, in order to efficiently provide various media contents of IPTV to users, we designed the emotion classification system for utilizing the emotion information of the media content. Next, we proposed 'personalized media contents curation system based on emotion information' for organizing the media contents, through the various processing steps. Finally, to demonstrate the effectiveness of this system, we conducted a user satisfaction survey(72.0 points). In addition, the results of comparing the results based on popularity and the results of the proposed system showed that the ratio leading to the actual users' viewing behavior was 10 times higher.

• The KIPS Transactions:PartB
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• v.8B no.6
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• pp.609-616
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• 2001

Today, as the Internet is popularized, information and news retrieval are generalized. However due to the tremendous amount and variety of information, many users appeal the difficulties of information retrieval. Thus in this paper, we propose a news retrieval system, which filters news articles using an intelligent agent with the learning ability of BPN (back propagation neural network). This system also uses a profile to accomodate the personalized news retrieval. This system consists of two major agents, collection agent and learning agent. The collection agent gathers the articles from several news sites, analyzes them, and stores into a database. The learning agent builds the BPN based on the personalized data. In addition, considering the popularity of the wireless internet due to the rapid development of communication technologies, we made this system provide the service through the wireless internet.

• Proceedings of the Korea Contents Association Conference
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• 2008.05a
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• pp.725-731
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• 2008

In this paper, we propose a novel construction scheme for the personalized e-Learning system based on IRT(item response theory), which can be applied to the content including non-hierarchical and horizontal learning objects in its learning nodes. Especially the proposed system performs tests and re-estimates examinee ability during the learning nodes are operating so that the results are directly applied to the next node. This scheme can be called a dynamic relationship between test and learning which is totally different from conventional customization based on learning procedures separated from test steps. Moreover, we should periodically modify the averages of node difficulties, item parameters, and ability parameters of students so that the system have more accurate personalized learning capability. As a result, this scheme maximizes learning efficiency offering the most appropriate learning objects and items to the individual students according to their estimated abilities and the system itself should obtain continuous improvements by modifying the parameters and fulfilling periodical feedbacks.

• Journal of KIISE:Computing Practices and Letters
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• v.11 no.2
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• pp.112-123
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• 2005

The latest E-commerce sites provide personalized services to maximize user satisfaction for Internet user The collaborative filtering is an algorithm for personalized item real-time recommendation. Various supplementary methods are provided for improving the accuracy of prediction and performance. It is important to consider these two things simultaneously to implement a useful recommendation system. However, established studies on collaborative filtering technique deal only with the matter of accuracy improvement and overlook the matter of performance. This study considers representative attribute-neighborhood, recommendation textile set, and similarity grouping that are expected to improve performance to the recommendation agent system. Ultimately, this paper suggests empirical applications to verify the adequacy and the validity on this system with the development of Fashion Design Recommendation Agent System (FDRAS ).

• Journal of the Korea Institute of Information and Communication Engineering
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• v.12 no.1
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• pp.46-52
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• 2008

In this paper, we present the Mont-Saint-Michel virtual reality system designed to create the virtual heritage environment, which is enriched with personalized tour guide service. The tour guide system allows users to travel in the virtual heritage site and get more information about the sites or items of user's interests. It also allows users to make their own tour guidebook with the pictures they have taken during the virtual tour and more detail descriptions from the tour guide database. It then generates the web-based tour guidebook for users to utilize it for the actual site visit or share it with others over the Internet. The components of this system are designed with the consideration of reusability to be used for other interactive tour guide systems. This paper describes the motivation the development and a preliminary user study of Mont-Saint-Michel virtual reality personalized tour guide system.

• Journal of Convergence for Information Technology
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• v.8 no.6
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• pp.143-148
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• 2018

In this paper, we propose a personalized user authentication system (PUAS) that can be used in multiple stages in user authentication by customizing the password keyword to be used in user authentication. The proposal concept is that the user oneself defines the password keyword to be used in user authentication so as to cope with a passive retransmission attack which reuses the password obtained when the server system is accessed in user authentication. The authentication phase is also designed so that it can be expanded in multiple stages in a single step. Also, it is designed to store user-defined password related information in an arbitrary encrypted place in the system, thereby designing to disable the illegal access of the network. Therefore, even if an intruder accesses the system using the proposed system, it is possible to generate personal authentication information by generating a password keyword through unique personal information possessed only by an individual and not know the place where the generated authentication information is stored, It has a strong security characteristic.

• Journal of the Korea Society of Computer and Information
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• v.8 no.4
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• pp.92-103
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• 2003

Great many advertisements are serviced in on-line by development of electronic commerce and internet user's rapid increase recently. However, this advertisement service is stopping in one-side service of relevant advertisement rather than doing users' inclination analysis to basis. Therefore, want advertisement service that many websites are personalized for efficient service of relevant advertisement and service through relevant server's log analysis research and enforce. Take advantage of log data of local system that this treatise is not analysis of server log data and analyze user's Preference degree and inclination. Also, try to propose advertisement system personalized by making relevant site tributary category and give weight of relevant tributary. User's preference user preference which analysis is one part of cooperation fielder ring of web personalized techniques use information in visit site tributary and suppose internet user's action in visit number of times of relevant site and try inclination analysis of mixing form. Express user's preference degree by vector, and inclination analysis result uninterrupted data that simplicity application form is not regarded and techniques that propose inclination analysis change of data since with move data use and analyze newly and proposed so that can do continuous renewal and application as feedback Sikkim. Presented method that can choose advertisements of relevant tributary through this result and provide personalized advertisement service by applying process such as user inclination analysis in advertisement chosen.