研究生: |
Ratih Nur Esti Anggraini Ratih - Nur Esti Anggraini |
---|---|
論文名稱: |
Conflict of Interest (COI) Detector in Reviewer Recommendation System using Potential Friendship Finder Conflict of Interest (COI) Detector in Reviewer Recommendation System using Potential Friendship Finder |
指導教授: |
李漢銘
Hahn-Ming Lee |
口試委員: |
Tyng-Ruey Chuang
Tyng-Ruey Chuang Wei-Chung Teng Wei-Chung Teng Tien-Ruey Hsiang Tien-Ruey Hsiang Jan-Ming Ho Jan-Ming Ho |
學位類別: |
碩士 Master |
系所名稱: |
電資學院 - 資訊工程系 Department of Computer Science and Information Engineering |
論文出版年: | 2012 |
畢業學年度: | 100 |
語文別: | 英文 |
論文頁數: | 51 |
中文關鍵詞: | confllict of interest 、reviewer recommendation system 、potential link finder. |
外文關鍵詞: | confllict of interest, reviewer recommendation system, potential link finder. |
相關次數: | 點閱:292 下載:0 |
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Conflict of Interest (COI) detection has become a significant issue in reviewer recommendation system because it can cause bias in the actor decision during reviewing process. Therefore, it is important to find relationships between actors in the network to avoid COI. Unfortunately, due to incomplete information in the network or for privacy reason, we can not detect all of those relationship.
In this thesis, we proposed Conflict of Interest (COI) Detector in Reviewer Recommendation System. Our approach can detect four types of COI: 1) COI in Coauthorship; 2) COI in Potential Friendship; 3) COI in Academic Ranking; and 4) COI in Institution. Potential Friendship Finder intends to find potential link between nodes in the network. It also can be used to suggest future collaboration to the expert.
The experiment results show that our proposed approach have a high recal rates and can detected more than 95% COI from a list of reviewers in recommendation system. The system also can adapt well to various network topologies.
Conflict of Interest (COI) detection has become a significant issue in reviewer recommendation system because it can cause bias in the actor decision during reviewing process. Therefore, it is important to find relationships between actors in the network to avoid COI. Unfortunately, due to incomplete information in the network or for privacy reason, we can not detect all of those relationship.
In this thesis, we proposed Conflict of Interest (COI) Detector in Reviewer Recommendation System. Our approach can detect four types of COI: 1) COI in Coauthorship; 2) COI in Potential Friendship; 3) COI in Academic Ranking; and 4) COI in Institution. Potential Friendship Finder intends to find potential link between nodes in the network. It also can be used to suggest future collaboration to the expert.
The experiment results show that our proposed approach have a high recal rates and can detected more than 95% COI from a list of reviewers in recommendation system. The system also can adapt well to various network topologies.
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