臺灣大學: 資訊網路與多媒體研究所鄭卜壬黃彥傑Huang, Yen-ChiehYen-ChiehHuang2013-03-222018-07-052013-03-222018-07-052012http://ntur.lib.ntu.edu.tw//handle/246246/251135搜尋字的意圖其實是跟搜尋的時間有關係的。 例如,搜尋字Yahoo在早上可 能是意圖尋找Yahoo! Map,而到了晚上時則是意圖尋找Yahoo! Game。 基於這個 發現,我們將搜尋字的意圖延伸為『意義』。 我們相信每個搜尋動作都有他背後 的意義,並且這個意義會根據時間不同。 如果搜尋引擎可以正確地在不同時間 識別這些意義,那麼結果的排序可能可以更準確的達到使用者的需求。 因此在 這篇論文中,我們透過click-through data去幫助搜尋引擎尋找這些在某個時間點的 意義。 我們提出了三種Log-Based的機率模型去依照不同時間排序不同的搜尋結 果。 此外,當原本的資料並不可靠時,我們也透過尋找『意義』去幫助這些機率 模型排序搜尋結果。 實驗結果顯示出我們的模型可以將結果排序的比原本AOL提 供的排序更要準確。The intent of a query is sometimes related to the time issued. For example, a query Yahoo may refer to Yahoo! Maps in the morning and Yahoo! Games in the evening. Based on this observation, we extend the search intent to a larger scale: sense. We believe that each search action has its own sense, and this sense differs in different time. If search engines could distinguish such sense by time, then the ranking produced will match a user’s information need more precisely. In this work, we use click-through data to help the search engines find the sense of the search action in a specific time. We build three log-based probabilistic models to rank the search results by time. In addition, we propose ”Sense” to help these models to rank more precisely when the log data is unreliable. Experimental results shows our models rank better then the original ranking from AOL data.140 bytestext/htmlen-US機率式檢索模型時間敏感時間敏感搜尋字Time-Sensitiveprobabilistic retrieval modelthesishttp://ntur.lib.ntu.edu.tw/bitstream/246246/251135/1/index.html