Robust and High-Accessibility Ranking Method for Crowdsourced Preference Sequences

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姓名

阮潘英輝(Nguyen Phan Anh Huy) 查詢紙本館藏

畢業系所

企業管理學系

論文名稱

Robust and High-Accessibility Ranking Method for Crowdsourced Preference Sequences
(Robust and High-Accessibility Ranking Method for Crowdsourced Preference Sequences)

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摘要(中)

隨著社群網站與共享經濟的興起，Crowdsourcing 資料已在多個領域被廣泛使用。而經由網民提供的喜好資料以可做為產品或服務排序的基礎。但是綜合網民所提供的互相衝突且不齊全的資料獲取正確排序是相當複雜的議題。為了解決這個問題，本研究提出一個新的演算法。本方法修改與增強FCM以達到高可靠性與高使用性。為了驗證可靠性、使用性與排序正確性，本研究並包含一系列的實驗，實驗資料包含真實資料與人造資料。實驗結果顯示本研究較其他方法有較好的可靠性與使用性。而正確性也與最好的Borda Count 不分軒輊.

摘要(英)

With the rapid development of social network and online services, crowdsourcing data
has been used for many solutions in various fields. The preference sequences obtained
through crowdsourcing are valuable resources for ranking. However, the aggregation of
incomplete and inconsistent preferences is complicated. To address these challenges, this
research proposed a novel method termed robust crowd ranking (RCR) based on a consistent
Fuzzy C-means (CFCM) approach to increase the robustness and accessibility of aggregated
preference sequences obtained through crowdsourcing. To verify the robustness, accessibility,
and accuracy of RCR, comprehensive experiments were conducted using synthetic and real
data. The simulation results validated that the RCR outperforms Borda Count, Dodgson, IRV
and Tideman methods.

關鍵字(中)

★ 排行
★ 靠性與高使用性
★ 使用性與排序正確性

關鍵字(英)

★ fuzzy c-means
★ accessibility,
★ crowdranking
★ Robust ranking
★ crowdsourcing

論文目次

ABSTRACT i
ACKNOWLEDGEMENT ii
TABLE OF CONTENTS iii
LIST OF FIGURES iv
LIST OF TABLES v
Explanation of symbols and abbreviations vi
Chapter 1. Introduction 1
1.1 Research background information 1
1.2 Contributions of the proposed model 3
1.3 Organization of the dissertation 3
Chapter 2. Related work 4
2.1 Crowdsourcing 4
2.2 Ranking Methods 7
Chapter 3. Methodology of Crowd Ranking 10
3.1 Preference Relation Matrix 11
3.2 FCM Based on Saaty Vectors 12
3.3 Converting a Centroid Into a Ranking Sequence 13
3.4 Identifying the Initial Centroid 16
Chapter 4. Experimental results for RCR 22
4.1 Robustness 23
4.1.1 Robustness Tests with synthetic data 24
4.1.2 Robustness tests using real data sets 28
4.2 Accessibility 30
4.3 Evaluation of Ranking Accuracy for Real Data Sets 32
Chapter 5. Conclusion and future research 34
5.1 Conclusion 34
5.2 Research Limitation and Future Research 35
References 36

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指導教授

許秉瑜(Hsu Ping-Yu)

審核日期

2018-7-27

推文