• Proceedings of the Korea Information Processing Society Conference
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• 2008.05a
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• pp.235-238
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• 2008

최근 모바일 Broadcasting 환경에서의 캐시 관리 기법에 관한 다양한 연구가 활발히 진행되고 있으며 가장 많이 쓰이는 기법으로는 무효화 보고(Invalidation Report) 기법을 들 수 있다. 하지만 무선 기기들의 대용량 지원 및 많은 사용자의 무선 이용으로, 사용자 요청에 대한 지연이나 Multi-Cell 환경과 대용량 갱신 등에 대한 대처 능력의 부족 등이 문제점으로 부각되고 있다. 본 연구는 이런 무효화 보고의 대처 능력을 보완할 뿐 아니라, User Preference를 추가하여 사용자의 QoS를 만족시키는 새로운 시스템을 제안한다. 본 연구는 서버 측에서의 일방적인 브로드캐스팅에 의한 데이터 전송이 아닌, 사용자로부터의 요청에 따른 캐시 데이터 관리 기법을 제안한다. 연구의 주된 효과는 사용자로 하여금 선택적 청취(Selective Listening)을 하게 함으로써 서버와의 교류를 적게 하고, 자주 사용하는 많은 양의 데이터를 한번에 가져와 빠른 시간 내에 데이터를 사용할 수 있게 한다. 또한, 자신이 필요한 데이터에 한에서만 자료 갱신(Update) 여부를 확인하여, 짧은 시간 안에 동적으로 자신의 정보를 확인 할 수 있다.

• KIISE Transactions on Computing Practices
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• v.22 no.10
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• pp.504-512
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• 2016

In this paper, we propose a personalized event recommendation scheme using user's activity analysis and collaborative filtering in social network environments. The proposed scheme predicts un-evaluated attribute values through analysis of user activities, relationships, and collaborative filtering. The proposed scheme also incorporates a user's recent preferences by considering the recent history for the user or context-aware information to precisely grasp the user's preferences. As a result, the proposed scheme can recommend events to users with a high possibility to participate in new events, preventing indiscriminate recommendations. In order to show the superiority of the proposed scheme, we compare it with the existing scheme through performance evaluation.

• Proceedings of the IEEK Conference
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• 2003.11b
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• pp.211-214
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• 2003

In this paper, we intend to find user's TV program choosing pattern and, recommend programs that he/she wants. So we suggest item dependency map which express relation between chosen program. Using an algorithm that we suggest, we can recommend an program, which a user has not saw yet but maybe is likely to interested in. Item dependency map is used as patterns for association in hopfield network so we can extract users global program choosing pattern only using users partial information. Hopfield network can extract global information from sub-information. Our algorithm can predict user's inclination and recommend an user necessary information.

• Journal of applied mathematics & informatics
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• v.28 no.1_2
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• pp.439-450
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• 2010

In this paper, we propose a new idea to evaluate the prediction accuracy of user's preference generated by memory-based collaborative filtering algorithm before prediction process in the recommender system. Our analysis results show the possibility of a pre-evaluation before the prediction process of users' preference of item's transaction on the web. Classification functions proposed in this study generate a user's rating pattern under certain conditions. In this research, we test whether classification functions select users who have lower prediction or higher prediction performance under collaborative filtering recommendation approach. The statistical test results will be based on the differences of the prediction accuracy of each user group which are classified by classification functions using the generative probability of specific rating. The characteristics of rating patterns of classified users will also be presented.

• Journal of Broadcast Engineering
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• v.24 no.3
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• pp.515-526
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• 2019

In the real world, the user's preference for a particular product is determined by many factors besides the quality of the product. The reflection of these external factors was very difficult because of various fundamental problems including lack of data. However, access to external factors has become easier as the infrastructure for public data is opened and the availability of evaluation platforms with diverse and vast amounts of data. In accordance with these changes, this paper proposes a recommendation system structure that can reflect the collectable factors that affect user's preference, and we try to observe the influence of actual influencing factors on preference by applying case. The structure of the proposed system can be divided into a process of selecting and extracting influencing factors, a process of supplementing insufficient data using sentence analysis, and finally a process of combining and merging user's evaluation data and influencing factors. We also propose a validation process that can determine the appropriateness of the setting of the structural variables such as the selection of the influence factors through comparison between the result group of the proposed system and the actual user preference group.

• The Journal of Society for e-Business Studies
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• v.9 no.2
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• pp.109-121
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• 2004

In this paper, we propose a system that generates a new customizing information for customer with classification and analysis in detail and provides customized information to individual customers automatically. A proposed system generate preference information and preference e-mail format as analysis and calculate that e-mail open rate and mouse event information. Using generated interesting information and preference e-mail format, individual customer's interest information according to e-mail standard and format that customer prefers through agent automatically recompose and push to customer. From experiment, the designed and implemented system showed high e-mail open ratio and user's satisfaction in performance assessment.

• 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.

• Journal of Korea Multimedia Society
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• v.14 no.11
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• pp.1467-1477
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• 2011

This paper proposes a HPPS(Hybrid Preference Prediction System) design using the analysis of user profile and of the similarity among users precisely to predict the preference for custom-tailored service. Contrary to the existing NBCFA(Neighborhood Based Collaborative Filtering Algorithm), this paper is designed using these following rules. First, if there is no neighbor's commodity rating value in a preference prediction formula, this formula uses the rating average value for a commodity. Second, this formula reflects the weighting value through the analysis of a user's characteristics. Finally, when the nearest neighbor is selected, we consider the similarity, the commodity rating, and the rating frequency. Therefore, the first and second preference prediction formula made HPPS improve the precision by 97.24%, and the nearest neighbor selection method made HPPS improve the precision by 75%, compared with the existing NBCFA.

• Journal of Information Technology Services
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• v.11 no.4
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• pp.181-194
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• 2012

The one of the most meaningful change in the recent ubiquitous environment is the omnipresence of public digital display device for providing ubiquitous information. It is the important issue to provide publicity as well as adaptive information to each user in the field of the public digital display device. This research proposes the idea ensuring fast response speed by the selection of user preference function. The preference function is selected by statistics using Zipf distribution in the system comprising mobile device and digital display device based on NFC (Near Field Communication). The idea is proved by CPM-GOMS model and the improvement of user response can be achieved.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.2
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• pp.164-171
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• 2006

Recently Digital multimedia broadcasting (DMB) has been available as a commercial service. The users sometimes have difficulty in finding their preferred multimedia contents and need to spend a lot of searching time finding them. They are even very likely to miss their preferred contents while searching for them. In order to solve the problem, we need a method for recommendation users preferred only minimum information. We propose an algorithm and a system for recommending users' preferred contents using preference transition probability from user's usage history. The system includes four agents: a client manager agent, a monitoring agent, a learning agent, and a recommendation agent. The client manager agent interacts and coordinates with the other modules, the monitoring agent gathers usage data for analyzing the user's preference of the contents, the learning agent cleans the gathered usage data and modeling with state transition matrix over time, and the recommendation agent recommends the user's preferred contents by analyzing the cleaned usage data. In the recommendation agent, we developed the recommendation algorithm using a user's preference transition probability for the contents. The prototype of the proposed system is designed and implemented on the WIPI(Wireless Internet Platform for Interoperability). The experimental results show that the recommendation algorithm using a user's preference transition probability can provide better performances than a conventional method.