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研究生:王志瑋
研究生(外文):Chih-WeiWang
論文名稱:數位口碑探勘法於商品推薦之研究
論文名稱(外文):Mining Digital Word of Mouth for Product Recommendation
指導教授:李昇暾李昇暾引用關係
指導教授(外文):Shen-Tun Li
學位類別:碩士
校院名稱:國立成功大學
系所名稱:資訊管理研究所
學門:電算機學門
學類:電算機一般學類
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:63
中文關鍵詞:推薦系統多準則決策模糊逼近理想解排序法具信任基礎之逼近理想解排序法信任基礎推薦系統意見探勘情感分析社群信任網絡
外文關鍵詞:Recommendation SystemMultiple Attribute Decision MakingFuzzy TOPSISTrust-based Fuzzy TOPSISTrust-based Recommendation SystemOpinion MiningSentiment AnalysisSocial Trust Relationship
相關次數:
  • 被引用被引用:2
  • 點閱點閱:302
  • 評分評分:
  • 下載下載:1
  • 收藏至我的研究室書目清單書目收藏:1
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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