臺灣博碩士論文加值系統

現存已有許多旅遊推薦系統有結合行動社群網絡，且為了因應系統內漸增的使用者，系統會透過在雲端上平行運算的特性來改善系統運算時間及效能，但這些系統大多還未能妥善應用在社群網絡中使用者所分享的文字，包括分析社群關係以及使用者的旅遊喜好，並進而提供客製化的景點資訊。除此之外，由於整體的社群關係會僅因為一位使用者更新了旅遊經驗而受到影響，這在系統運算整體社群關係時，即使使用平行運算來節省運算時間，也會造成嚴重的重複運算問題[7] [8]。在此研究中，提出了一個可以根據使用者的旅遊喜好來智慧推薦景點資訊的系統，透過系統中半結構式的使用者介面及知識本體論，可利用使用者上傳的部落格，有效地分析出使用者的旅遊喜好，進而將使用者依旅遊喜好群組化，以此群組來提供客製化的景點資訊。除此之外，此系統還將分群的結果融入雲端運算中來消弭更新分群時所碰到的重複運算問題。在實驗數據中，分析了在不同變數下群組化的趨勢，以及驗證了融合分群到雲端運算中可以有效的提高系統運算速度，卻不影響到分群的群內相似度。

Although many touring recommendation systems work with mobile social network, the delivered posts among users are still not to be utilized well for estimating user’s travel preferences. Besides, with the increasing of users in touring recommendation systems, using the parallel cloud computing can help save executing time and computing resources. However, the processing of estimating user’s travel preferences results in the severe redundancy problem [7][8], because when one user updates his/her travel experiences, the social relationship among all users will be changed synchronously. In this thesis, a cloud-based touring recommendation system with mobile social network, i.e., Intelligent Touring Recommending System (ITRS) is proposed, to recommend users suitable Point of Interests (PoIs) according to users’ travel preferences. Users are classified into meta-groups by analyzing the blogs through the proposed semi-structured user interface and ontology. Besides, the meta-groups mechanism is combined with the cloud computing model to reduce the redundancy problem existed in executing. In the experiment, results demonstrates the trend of classifying meta-groups results. Moreover, the experiment results also have shown the better efficiency of the proposed cloud model, for which the proposed cloud model still keeps the group’s internal similarity as good as the traditional ones.

Acknowledgements III
Contents IV
List of Figures V
List of Tables VI
Chapter 1 Introduction 1
Chapter 2 Related Works 5
2.1 Cloud Computing Techniques 5
2.2 The Cloud-based Recommendation System 6
Chapter 3 Problem Statement 9
3.1 Redefining Social Relationship 9
3.2 The Redundancy Problem in Executing 11
Chapter 4 The Proposed Function and Model 14
4.1 The Criterion of User Similarity 14
4.2 The Fusion of the Cloud Model and Meta-group 16
Chapter 5 Architecture of ITRS 19
5.1 Three Layers in ITRS 19
5.2 The Components in ITRS 20
a) Semi-structured User Interface 21
b) Text Inference Model (TIM) 22
c) Meta-group Classifier 23
Chapter 6 Performance Analysis 25
6.1 The Trend of Criterion Function of User Similarity 25
6.2 The Recommending Results of Proposed Cloud Model 28
6.3 The Comparison of Group’s Internal Similarity 30
Chapter 7 Conclusions 33
Bibliography 34

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