在本論文中，我們對虛擬關聯回饋之這個步驟都進行了探討與改進。在虛 擬關聯文件選取方法中，本論文題出將文件之關聯性資訊融入虛擬關聯文件選 取中，藉此得到以往以查詢關連為主的方法中所遺漏之資訊。此方法之參數調 整亦較現有的虛擬關聯文件選取方法有利。實驗結果顯示以此方法所挑選之虛 擬關聯文件所進行的虛擬關連回饋有不錯的效果，且在不同的虛擬關聯文件使 用數量及不同數量的候選文件的情況下都能發揮效果，表示該方法能如我們所 預期的選出有效的虛擬關聯文件，且在調整參數之效率及檢索強健性上都有所 改善。
在查詢模型上，我們探討現有 SMM 及 RMM 之不足之處，並加以改善，
以求強化查詢模型之效果。雖然在實驗結果上並未都有所改善，但在某些設定 中亦對整體檢索有所幫助，顯示這些查詢模型仍有改善空間。
我們亦探究了清晰度的計算及其所代表的意義，並將其作了除了原本的效 能評估以外之應用，即將其用於虛擬關聯文件回饋模型輔助資訊檢索中。而實 驗中亦證明其結果能夠逼近甚至超越以往只能靠手動調整之結果，並讓其調整 除了能針對不同查詢而自動作出修正外，更具有理論意義上的基礎。
在本論文中仍有許多需要研究及探討的方向：在虛擬關聯文件選取方面，
目前計算文件間關聯之方法是以餘弦相似性為主，而此方法只考慮兩文件模型 中之詞分布情形。未來我們可以考慮利用更多不同種類之資訊來估測文件關聯 性，如多連詞或詞間距離等面向都十分具有潛力。在查詢模型之改善上，由於 我們所改進的模型大都著重於高查詢關聯之文件上，故我們可針對其特性找到 符合其特性之虛擬關聯文件選取方法，以讓其能充分利用這些資訊。在清晰度 的資訊方面，由於我們目前只採用虛擬關聯文件集合之模型與背景間之距離來 進行清晰度的估測，並未使用到其他查詢或回饋模型中的各種特徵；因此，未
來我們希望能考慮更多不同的清晰度估測方式並將其用以輔助查詢模型之結 合權重調整，以達到根據不同查詢而有不同權重的目的。
在本論文中，我們也發現，在進行虛擬關聯文件選取時，候選文件的數量 以及使用的文件數量對整體檢索結果有很大的影響。由於不同的虛擬關聯文件 選取方法亦有不同的最佳的候選文件數量及使用之文件之組合，故在未來的研 究中，我們也可以朝著如何決定前面所述之選取範圍以及更強健的虛擬關聯文 件選取方法的方向進行研究。
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