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研究生:Anindhita Dewabharata
研究生(外文):Anindhita Dewabharata
論文名稱:研發與設計以語意網先進技術為基礎之觀光醫療建議系統
論文名稱(外文):A Design of Semantic-based Recommender System for Medical Tourism
指導教授:周碩彥周碩彥引用關係
指導教授(外文):Shuo-Yan Chou
口試委員:周碩彥
口試日期:2012-07-04
學位類別:碩士
校院名稱:國立臺灣科技大學
系所名稱:工業管理系
學門:商業及管理學門
學類:其他商業及管理學類
論文種類:學術論文
論文出版年:2012
畢業學年度:100
語文別:英文
論文頁數:48
中文關鍵詞:Medical TourismRecommender SystemSemantic WebOntologySemantic Association
外文關鍵詞:Medical TourismRecommender SystemSemantic WebOntologySemantic Association
相關次數:
  • 被引用被引用:0
  • 點閱點閱:216
  • 評分評分:
  • 下載下載:26
  • 收藏至我的研究室書目清單書目收藏:0
Medical tourism has been growing very rapidly in recent years. This trend causing the information about medical tourism destination will increase significantly. The information of medial tourism has been found online started from the demographic spread of the potential medical tourists and medical destination. However, the growth of information available on the web nowadays has led to information overload, hampering the user's ability to distinguish relevant information from irrelevant. This condition restricts people use information resource effectively.
Due to this fact, recommender systems have gained momentum as an efficient tool to reduce the complexity when searching for relevant information. Personalization capabilities are undoubtedly valuable for recommender system to match the user's preference against all available medical tourism resources. In designing a recommendation system, it is important to consider about construction of the main design decisions and it can be constrained by the environment of the recommender which is influence them. The recommender system is designed by using the technology of the semantic web to model the domain knowledge and as a content-based recommendation technique.
Finally, a design of recommender system for medical tourism has been proposed in this research. The system will generate recommendation of medical tourism resources all in one package to users.
Medical tourism has been growing very rapidly in recent years. This trend causing the information about medical tourism destination will increase significantly. The information of medial tourism has been found online started from the demographic spread of the potential medical tourists and medical destination. However, the growth of information available on the web nowadays has led to information overload, hampering the user's ability to distinguish relevant information from irrelevant. This condition restricts people use information resource effectively.
Due to this fact, recommender systems have gained momentum as an efficient tool to reduce the complexity when searching for relevant information. Personalization capabilities are undoubtedly valuable for recommender system to match the user's preference against all available medical tourism resources. In designing a recommendation system, it is important to consider about construction of the main design decisions and it can be constrained by the environment of the recommender which is influence them. The recommender system is designed by using the technology of the semantic web to model the domain knowledge and as a content-based recommendation technique.
Finally, a design of recommender system for medical tourism has been proposed in this research. The system will generate recommendation of medical tourism resources all in one package to users.
Abstract i
Acknowledgements ii
Table of Contents iii
List of Figures iv
List of Tables v
Chapter 1 Introduction 1
1.1 Background and Motivation 1
1.2 Objective 3
1.3 Methodology 4
1.4 Organization of the Thesis 4
Chapter 2 Literature Review 5
2.1 Medical Tourism 5
2.2 Semantic-based Recommender System 8
2.3 Ontology 9
2.4 Semantic Association 11
Chapter 3 Semantic-based Model 15
3.1 Environmental Models 15
3.2 The Domain Ontology 17
3.3 Content-based Strategy 24
3.3.1 Filtering Phase 25
3.3.2 The Recommendations Phase 27
3.3.3 The Information Feedback 28
3.3.4 A Sample Scenario 29
Chapter 4 The Prototype of Semantic-based Recommender System 31
4.1 Architecture 31
4.2 Data Management 34
4.3 User-System Interaction 36
Chapter 5 Conclusion and Future Research 43
References 45
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