臺灣博碩士論文加值系統

隨著網際網路的蓬勃發展，許多使用者常常透過網際網路尋找產品規格與評論作為採購前的參考，但網路上常充斥著大量垃圾訊息，因此使用者通常無法在很短的時間內獲取關鍵資訊。為了解決這項問題，已許多學者提出了各式各樣的推薦技術來協助使用者進行產品的選擇。舉凡，特徵推薦、偕同過濾推薦、內容推薦、問卷推薦、知識推薦與信任推薦等方法。然而，目前的推薦技術仍然無法滿足使用者同時對方案規格與品質的要求。因此，本研究將以手機推薦為例提出一套具有信任基礎的推薦方法，來解決上述的問題。本研究提出的方法，主要是以模糊多準則決策、意見探勘與社群信任關係進行概念整合，提出一套同時考量方案規格符合度與口碑信賴因素的推薦方法，以補足現今推薦系統的各項缺陷，並供後續研究者做延伸探討。

With the advent of internet, many users usually search the product specification and reviews as the reference before buying by internet. Unfortunately, there exists a lot of spam information that users cannot find the key information in a short time. Therefore, some researchers have proposed a variety of recommendation methods to solve this problem, such as Feature-based, Collaborative filtering-based, Content-based, Demographic-based, Knowledge-based, Trust-based, etc. However, the most of methods still cannot fulfill the user need of product features quality and reliability simultaneously. So this study proposed trust-based fuzzy TOPSIS method to improve this problem. To reach our target, our method includes of fuzzy MADM, opinion mining, and social trust scores. This study also hopes our proposed method could provide researcher with some idea to extend.

摘要 II
Abstract III
Acknowledgement IV
List of Tables VIII
List of Figures IX
Chapter 1 Introduction 1
1.1 Background 1
1.2 Research Motivation 2
1.3 Research Objective 3
1.4 Scope and Limitations 3
1.5 The Process of the Research 4
Chapter 2 Literature Review 5
2.1 Information Retrieval 5
2.1.1 Web Crawler 5
2.1.2 Chinese Knowledge Information Processing (CKIP) 6
2.2 Multiple Criteria Decision Making 6
2.3 Fuzzy Theory 12
2.3.1 Fuzzy Set 12
2.3.2 Fuzzy Number 13
2.3.3 Linguistic Variable 14
2.4 Fuzzy TOPSIS 16
2.5 Sentiment Analysis and Opinion Mining 16
2.5.1 Dictionary-based 17
2.5.2 Statistics-based 18
2.5.3 Machine Learning-based 19
2.6 Recommendation System Techniques 20
2.6.1 Collaborative Filtering-based 20
2.6.2 Content-based 21
2.6.3 Demographic-based 21
2.6.4 Feature-based 22
2.6.5 Knowledge-based 22
2.6.6 Trust-based 23
Chapter 3 Research Method 24
3.1 Data Preprocess 25
3.1.1 Web Crawling 25
3.1.2 Features Extraction 26
3.1.3 Sentiment Analysis 26
3.1.4 Expert Trust Scores Algorithm 28
3.2 Recommendation Process 31
3.2.1 Refine Request List Algorithm 31
3.2.2 Automatically generated weight Algorithm 32
3.2.3 Transform the Candidate Product List into Fuzzy MADM Matrix 33
3.2.4 Trust-based Fuzzy TOPSIS 34
3.3 Evaluation 37
Chapter 4 System Implementation and Evaluation Result 38
4.1 System Implementation 38
4.1.1 Development Environment 38
4.1.2 Data Collection 39
4.1.3 System Demonstration 42
4.2 System Evaluation and Analysis 50
Chapter 5 Conclusion 57
References 59

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