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研究生:劉發元
研究生(外文):Fa-YuanLiu
論文名稱:社群網路中基於階層式能力模型之最佳化溝通及人力成本的組隊問題
論文名稱(外文):Forming a Team of Cost-effective and Well-collaborated Experts in Social Networks Based on Hierarchical Skill Model
指導教授:黃仁暐
指導教授(外文):Jen-Wei Huang
學位類別:碩士
校院名稱:國立成功大學
系所名稱:電腦與通信工程研究所
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2017
畢業學年度:105
語文別:英文
論文頁數:49
中文關鍵詞:組隊問題專家探勘社群網路圖論演算法多目標最佳化
外文關鍵詞:Team FormationExpertise QuerySocial NetworkGraph AlgorithmsMulti- objective Optimization
相關次數:
  • 被引用被引用:1
  • 點閱點閱:100
  • 評分評分:
  • 下載下載:5
  • 收藏至我的研究室書目清單書目收藏:0
近年來,許多研究從不同角度探討了基於社群網路的組隊問題;然而在過去的研究中,大多僅考量少數影響組隊的因素。另外,這些研究中所使用的能力模型僅允許使用與專案需求相同技能的人才;而不考慮相近技能之人才。為了能更貼近現實生活中的組隊需求,我們的研究中除了考量兩項影響組隊結果最大的因素:溝通及人力成本外,更比起過往之研究探討更多可能影響組隊結果的因素。我們也提出了階層式能力模型,該能力模型允許團隊中擁有與專案需求相近技能的人才、而非只能選入專案所需技能的專家,以提高組隊的彈性。我們更提出了基於多目標最佳化理論的演算法,該演算法可根據使用者在單一目標下給定的預算,找出在另一目標下最佳化的團隊。這樣的演算法可解決在現實生活中,溝通成本與人力成本常常必須有所取捨的情況,並提供不同預算下的組隊建議。在實驗中不但證明了我們的問題假設是合理的,同時所提出的演算法也能比過去的研究找到成本更低且符合專案需求的團隊。
Social network-based team formation problem has been widely studied from different aspects. However, many of the previous studies only consider few factors while forming a team. The skills in earlier works were treated equally where assigning experts possess alternative skills for a required skill is not allowed. To better fit real word scenarios, our work considers more factors including the most important two: communication cost and personnel cost. We propose a novel hierarchical skill model to let skills interchangeable. We also develop a two-step optimization framework under previous settings to deal with the trade-off between communication cost and personnel cost. It lets users could form a team based on their desired budget on one of the costs. Finally the experiments show that our proposed framework and skill model is reasonable and have better performance than earlier works.
中文摘要 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . i
Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ii
Acknowledgment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iii
Table of Contents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iv
List of Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vi
List of Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii
1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
2 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
3 Problem Formulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
3.1 Preliminaries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
3.2 Problem Formulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
4 Hierarchical Skill Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
4.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
4.2 Covering Relation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
4.3 Skill Hierarchy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
4.4 HSM-TF Problem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
5 Proposed Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
5.1 Data Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
5.2 Finding Best Team with Bounded Communication Cost . . . . . . . . . . . . . . 24
5.2.1 Selection of Initial Nodes . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
5.2.2 Spanning the Team . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
5.3 Finding Pareto-optimal Teams . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
6 Performance Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
6.1 Dataset . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
6.2 Comparative Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
6.3 Experiment Settings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
6.3.1 Task Generation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
6.3.2 Metric . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
6.4 Results and Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40
7 Conclusions and Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47
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