臺灣博碩士論文加值系統

數位科技的迅速發展，使保險行業帶來革命性的改變，P2P保險平臺應運而生，以更低的成本使更多的潛在保險人參與。但目前P2P保險平台多缺乏能夠輔助使用者透過社群網路的力量找到共保人並形成共保群組的機制，因此本研究提出一個通過社群網絡形成社群共保小組的推薦系統，透過分析保險人的保險意願、保險人之間的關係與影響度，找到凝聚力最強的小組進行推薦，藉以提升P2P保險的用戶使用意願與滿意程度。

As P2P business model became more popular in the insurance industry, it properly brings out the core function of insurance: risk management in risk-sharing community. Nowadays, P2P insurance platform are prospering through financial technology, but most of them rarely utilize power of social networks to assist insurers to find their co-insurers. In addition, through current online platforms, it is difficult to find suitable co-insurers group without risk considering. In this research, we propose a social-based co-insurers recommendation mechanism through analyzing users’ inclination, posts, background, similarity, and relationship, to further improve the advantage of P2P insurance and reduce the risk in risk-sharing group.

摘要 I
ABSTRACT II
INDEX IV
List of Figures VI
List of Tables VII
Chapter 1 Introduction 1
1.1 Background 1
1.2 Motivation and Research Problems 2
1.3 Research Goals and Contributions 4
1.4 Thesis Outline 4
Chapter 2 Related Literatures 5
2.1 Peer-to-Peer Insurance 5
2.2 Risk Sharing and Social Trust 6
2.3 Social Influence and Decision Making 7
2.4 Recommendation Systems 8
Chapter 3 The System Framework 10
3.1 Individual Preference Analysis Module 12
3.1.1 Individual Preference Similarity Computing 12
3.1.2 Individual Risk Tolerance Similarity Computing 15
3.2 Social Relationship Analysis Module 16
3.2.1 Social Interaction Analysis 17
3.2.2 Social Closeness Computing 17
3.3 Social Influence Analysis Module 18
3.3.1 Insurance History Considering 19
3.3.2 Individual Information Considering 19
3.3.3 Social Trust Computing 20
3.4 Group Formation Module 21
3.4.1 Willingness Criteria Computing 21
3.4.2 Group Cohesion Computing 22
3.5 Group List Generation 25
Chapter 4 Experiments 27
4.1 Experiment Process 27
4.2 Data collection 29
4.2.1 User Profile 29
4.2.2 Insurance background and preference 31
4.3 Measurement Computing 32
4.3.1 Criteria Weight Computation 32
4.3.2 Suitability Computation 33
Chapter 5 Result and Evaluation 34
5.1 Test of Consistency 34
5.2 The Evaluation of Accuracy 35
5.2.1 Weight Evaluation 37
5.2.2 Recommendation Evaluation 37
5.2.3 Elimination Mechanism Evaluation 38
5.2.4 Misplace Evaluation 39
5.3 The Evaluation of Likeness, Satisfaction, and Willingness 40
5.3.1 The Evaluation of Likeness 40
5.3.2 The Evaluation of Satisfication 41
5.3.3 The Evaluation of Willingness 42
Chapter 6 Conclusion and Future Work 44
6.1 Research Summary 44
6.2 Research Contribution 45
6.3 Research Limitations 45
6.4 Future works 46
REFERENCE 47

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