臺灣博碩士論文加值系統

在社群問答網站中，有大量的問題被發問，但回覆率不高且有些問題需要長時間的等待才會有人來回答，甚至沒有人回答。為了提高回覆率，本研究利用aspect model在Quora這個社群問答網站做研究，當新的問題被發問，即依照Quora網站上每個使用者過去回答過的內容與發問過的問題來看是否適合回答此問題；實驗結果發現根據問題的相關主題尋找適合的回覆者是較為準確的。

A large number of questions are posted in CQA websites, but the chance of getting responses is usually low. Some questions need to wait for a long time before getting responses, even no responses. In order to increase the chance of getting responses, we use aspect model to recommend answerers when a question is raised. The recommendation is based on each user’s answered and raised questions in Quora (a famous CQA website). Our study find that recommendations according to questions’ related topics are more accurate.

致謝 I
摘要 II
Abstract III
目錄 IV
圖目錄 V
表目錄 VI
公式目錄 VII
第一章 緒論 1
1.1 研究背景與動機 1
1.2 研究問題與目的 3
第二章 文獻探討 5
2.1 社群問答網站尋找專家 5
2.2 自然語言工具(Natural Language Toolkit, NLTK) 7
第三章 研究方法 9
3.1 使用者間的關係 10
3.2 使用者與主題的關係 10
3.3 問題與主題的關係 12
第四章 實驗結果 13
4.1 實驗環境 13
4.2 實驗資料 13
4.3 資料處理 14
4.4 實驗結果 16
4.4.1 關鍵字取至30 次的結果 16
4.4.2 關鍵字取至25 次的結果 19
4.4.3 關鍵字取至20 次的結果 22
4.4.4 綜合三個實驗的結果 25
第五章 結論與未來展望 27
5.1 結論 27
5.2 未來展望 27
參考文獻 29

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