研究生: |
鈕諾亞 Navaraj - Neupane |
---|---|
論文名稱: |
Reviewer Recommendation using Academic Tag Comparison based on Boolean and Vector Space Model Reviewer Recommendation using Academic Tag Comparison based on Boolean and Vector Space Model |
指導教授: |
李漢銘
Hahn-Ming, Lee 何建明 Jan-Ming, Ho |
口試委員: |
Wei-Chung Teng
Wei-Chung Teng Tien-Ruey Hsiang Tien-Ruey Hsiang Tyng-Ruey Chuang Tyng-Ruey Chuang |
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: | Recommendation system 、academic tag 、boolean model 、vector space model |
外文關鍵詞: | Recommendation system, academic tag, boolean model, vector space model |
相關次數: | 點閱:419 下載:2 |
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Recommendation system is information filtering system that seek to predict the rating or preference that a certain query shows relevancy to particular item or document. In this thesis, we propose a recommendation system which assists journal and conference editors to find suitable reviewer for the proposal. Here, we have focused on domain classification issue of proposal recommendation system using academic tag comparison. We used different source of domain knowledge base called “Call for papers (CFP)”. Based on the keywords provided in CFPs we build domain to classify proposal and reviewers in particular domain. We used Boolean and Vector Space Model concept to find out relevant domain as well as relevant reviewers and ranked them. In our experiment we used real world dataset from National Science Council (NSC) Taiwan, which comprises of 724 proposals (Year 99) and 1253 reviewers. The experimental result shows that our system performs marginally better than previous Expert Finding System.
Recommendation system is information filtering system that seek to predict the rating or preference that a certain query shows relevancy to particular item or document. In this thesis, we propose a recommendation system which assists journal and conference editors to find suitable reviewer for the proposal. Here, we have focused on domain classification issue of proposal recommendation system using academic tag comparison. We used different source of domain knowledge base called “Call for papers (CFP)”. Based on the keywords provided in CFPs we build domain to classify proposal and reviewers in particular domain. We used Boolean and Vector Space Model concept to find out relevant domain as well as relevant reviewers and ranked them. In our experiment we used real world dataset from National Science Council (NSC) Taiwan, which comprises of 724 proposals (Year 99) and 1253 reviewers. The experimental result shows that our system performs marginally better than previous Expert Finding System.
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