本論文的主要目標是利用使用者輸入的Query Logs，搭配Basket-based和Item-based的機率模型實作一個關鍵字推薦系統。 研究一開始，我們先設計了小工具來蒐集使用者輸入的Query Logs ，以這些資料做為我們推薦的基礎，搭配機率模型經過計算後，產生一組推薦清單並依照分數排序再推薦給使用者。本系統推薦的關鍵字主要是屬於Query Expansion的類型，根據使用者輸入的關鍵字組合，找出其他對使用者有幫助的關鍵字，並加回原始的組合當中做為推薦的結果。 在實際請使用者實驗過後，我們利用Precision-Recall的方法來評估推薦的成效。透過統計數據可以發現，使用Basket-based和Item-based的機率模型來推薦關鍵字，的確是可以推薦出對使用者有幫助的關鍵字。 The main goal of this thesis is to design a query recommend system by utilizing users query logs combined with Basket-based and Item-based probabilistic model. At the beginning of the research, we design tools to collect the query logs entered by users, which is the basic of our recommendation, combined these with probabilistic model to create a recommendations list arranged by score sequence，and finally recommend it to the users. The system, which recommends keywords mainly belonging to the method of query expansion, will try to find other helpful keywords based on the users query and add these new words to the original query as the recommending results. After practically being used by users, we utilize Precision-Recall method to evaluate the results. Judging by statistic, we can find out that basket-based and Item-based probabilistic model successfully creates helpful key words recommendations to the users.
