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研究生: 鄭仲翔
Cheng, Chung-Hsiang
論文名稱: 基於社群參與者協同合作進行社群元素可信度之驗證
A Collaborative Trustworthiness Validation Model for Social Elements in Domain-Specific Community
指導教授: 焦惠津
Jiau, Hewi-Jin Christine
斯國峰
Ssu, Kuo-Feng
學位類別: 碩士
Master
系所名稱: 電機資訊學院 - 電腦與通信工程研究所
Institute of Computer & Communication Engineering
論文出版年: 2016
畢業學年度: 104
語文別: 英文
論文頁數: 66
中文關鍵詞: 社群元素可信度協同合作模型聲譽演算法使用者分類社群運算
外文關鍵詞: Social Element Trustworthiness, Collaborative Model, Reputation Algorithm, User Classification, Social Computing
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  • 社群參與者間的協同合作近期成為社群相關研究的重點之一,而參與者的協同合作可以促進社群的正向循環,此循環即吸引參與者加入社群、驅動參與者的互動、建立互信、最後參與者能互惠並互利。協同合作基礎為互信,而現今的社群系統皆須仰賴系統管理者對社群元素進行驗證以維持社群中的互信。管理者的手動驗證在處理使用者對於錯誤訊息的回報上的低效率效率,使其難以維持社群內參與者的互信。
    因此,此篇論文提出了一個協同合作可信任度驗證模型(CTVM)以計算社群參與者與其創作 (Creations) 之可信度,並藉此建立社群的互信。 CTVM 將社群參與者分為四個階層。 擁有較高可信度的參與者會被分到較高的階層,而相較於低階層參與者,高階層參與者會被賦予較多社群活動的權限。 高階層參與者可因此對社群做出更大貢獻。 此外,為了檢驗 CTVM 的有效性,此篇論文進行了兩項模擬,分別基於社群觀點以及參與者個人觀點。在基於社群觀點的模擬中,優質評論、評分的比例皆隨著時間演進持續增長。和一個基準模型比較,CTVM 仍然在優質評論、評分的比例上高於基準模型。 而在基於個人參與者觀點的模擬中,優質參與者和不良參與者分別都被歸類為高階層與低階層。在此模擬中亦發現,當優質參與者變成不良參與者,其階層會下降。而比起階層下降,階層上升會需要參與者進行更多正向的貢獻。 CTVM 可以促進對於物聯網以及社群網路中社群信任的成型,並協助社群演進並使社群能自我館利並永續經營。

    Collaboration among participants becomes a focus of researches in IoT and social network. Based on collaboration, a positive cycle that attracts participants, motivates interaction, builds trust, benefits participants and create profits continuously is formed. Social participants can be benefited from collaboratopn. Since collaboration among participants depends on trust, participants should be able to trust one another and other's creations. Existing systems rely on administrators to validate social elements to maintain trust. Manual validation cannot efficiently deal with reports and maintain trust among participants. Thus, a collaborative trustworthiness validation model (CTVM) is proposed to compute trustworthiness of participants along with their creations (i.e. comments and ratings) and build trust. CTVM classifies participants into four levels. Participants with higher trustworthiness are classified to higher levels. High-level participants are entrusted with more activity quota than low-level participants. Thus, higher level participants can contribute more to community. Two simulation tasks in the viewpoint of community and individual participant are adopted to verify effectiveness of CTVM. In community viewpoint, percentage of good comments and ratings continuously increase. By comparison with a baseline model, CTVM also outperforms it in percentage of comments and ratings. In participant viewpoint, good participants and bad participants can both maintain themselves at high level and low level, respectively. As good participants turn into bad participants, her level drop dramatically. It takes her more positive behavior to turn back to high level. CTVM is expected to foster trust inside social media and IoT and facilitate their evolution. Therefore, community self-governance and sustainability can be achieved.

    Chapter 1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.1 Collaboration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 1.2 Example of Eatery Dining Experience Sharing . . . . . . . . . . . . . . . . . . . . . . . . . 8 1.3 Problem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 1.4 Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 2 Collaborative Trustworthiness Validation Model (CTVM) . . . . . . . . . . . . . . . 17 2.1 Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 2.2 Hierarchical Regulation Model (HRM) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 2.3 Social Element Reputation Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 2.3.1 Initialization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 2.3.2 Objectivity Score . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 2.3.3 Consistency Score and Confidence Score . . . . . . . . . . . . . . . . . . . . . . . . 29 2.3.4 Iterative Computation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 2.3.5 Participant Reputation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 2.4 CTVM in Context of Foodie Dining Experience Sharing . . . . . . . . . . . . . . . . . . . 33 3 Evaluation of CTVM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 3.1 Simulation Tasks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 3.2 Community-based Viewpoint . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36 3.2.1 Number of Foodies and Comments . . . . . . . . . . . . . . . . . . . . . . . . . . . 38 3.2.2 Foodie Distribution over Four Levels . . . . . . . . . . . . . . . . . . . . . . . . . . 41 3.2.3 Comparison of CTVM and a Baseline Model . . . . . . . . . . . . . . . . . . . . . 44 3.3 Foodie-based Viewpoint . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 3.3.1 Foodie0 – Consistently Good Foodie . . . . . . . . . . . . . . . . . . . . . . . . . . 45 3.3.2 Foodie1 – Consistently Bad Foodie . . . . . . . . . . . . . . . . . . . . . . . . . . . 46 3.3.3 Foodie2 – From Good Foodie to Bad Foodie and Back to Good Foodie . . . . . . . 47 4 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55 4.1 Trust on Online Social Community . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55 4.2 Collaboration and Trust . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57 5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62

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