未來研究方向

本論文為了融入使用者目標的概念，使用 LSA 來導入概念空間 (Concept Space) 的觀念，獲得了初步成功。若能透過其他的方式來結合使用者概念跟文 件關鍵字，例如知識本體 (Ontology) 或語意網路 (Semantic Network)…等等，

使其獲得更準確的「使用者概念–文件關鍵字」關係，相信能獲得更進一步的 效果。另外，未來關於 SOM 模型仍可加以改良的部分，有下列兩點：

鄰近區域函式 (Neighborhood Function)：從 2.1 中所提到的方程式 3，

一個文件對其勝利點對其周圍的鄰近點，影響力的大小，對 SOM 的 分群結果應該是關鍵的。但如何將賦予其意義，做有效的應用，仍然 不甚清楚。

模型向量 (Model Vector)：SOM 的分群結果，能否用更具威力、具其 他意義的表示法，而不單單只是一個模型向量呢？關於這方面的研 究，非常的多。在本系統中，若從改良模型向量著手，或可將對於知 識專家非常有用的知識本體 (Ontology) 加入，使得系統成為更具實用 性。

最後，如何找到在 SOM 中可做使用者相關回饋機制的切入點，從而提出 有效的回饋法，也是我們未來努力的方向。

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