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  • 中文 을 입력하시려면 zhongwen을 입력하시고 space를누르시면됩니다.
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      KCI등재 SCOPUS

      A Recommendation System using Dynamic Profiles and Relative Quantification

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      https://www.riss.kr/link?id=A76130273

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      다국어 초록 (Multilingual Abstract)

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      Recommendation systems provide users with proper services using context information being input from many sensors occasionally under ubiquitous computing environment. But in case there isn't sufficient context information for service recommendation in spite of much context information, there can be problems of resulting in inexact result. In addition, in the quantification step to use context information, there are problems of classifying context information inexactly because of using an absolute classification course.
      In this paper, we solved the problem of lack of necessary context information for service recommendation by using dynamic profile information. We also improved the problem of absolute classification by using a relative classification of context information in quantification step.
      As the result of experiments, expectation preference degree was improved by 7.5% as compared with collaborative filtering methods using an absolute quantification method where context information of P2P mobile agent is used.
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      Recommendation systems provide users with proper services using context information being input from many sensors occasionally under ubiquitous computing environment. But in case there isn't sufficient context information for service recommendation in...

      Recommendation systems provide users with proper services using context information being input from many sensors occasionally under ubiquitous computing environment. But in case there isn't sufficient context information for service recommendation in spite of much context information, there can be problems of resulting in inexact result. In addition, in the quantification step to use context information, there are problems of classifying context information inexactly because of using an absolute classification course.
      In this paper, we solved the problem of lack of necessary context information for service recommendation by using dynamic profile information. We also improved the problem of absolute classification by using a relative classification of context information in quantification step.
      As the result of experiments, expectation preference degree was improved by 7.5% as compared with collaborative filtering methods using an absolute quantification method where context information of P2P mobile agent is used.

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      목차 (Table of Contents)

      • Abstract
      • 1. Introduction
      • 2. Related Works
      • 3. Recommendation System using Dynamic Profiles and Context
      • 4. Evaluation
      • Abstract
      • 1. Introduction
      • 2. Related Works
      • 3. Recommendation System using Dynamic Profiles and Context
      • 4. Evaluation
      • 5. Conclusion
      • References
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