隨著資訊科技的發達，不論是平面資訊或多媒體資料都有大幅度的成長，而如何在如此大量的資料中找出真正想要或感興趣的資料則是人們所面臨到的問題。推薦技術的方展則是為幫助人們在選擇上能夠更輕鬆、有效率的完成。以多媒體資料的推薦來說，在以往的研究中通常僅利用使用者對多媒體資料的評分或多媒體本身的特徵資料來做分析，而這些都各自有許多無法克服的問題存在，所以並不足以完全的幫助使用者取得他們真正想要的資料。在本研究中，我們提出了一個以整合約略集合方法為基礎並整合協同過濾資訊與內涵資訊的推薦方法，我們的方法主要考量了以下幾點：1).使用者分群，2).以關聯規則預測使用者評分，3).商品數量之簡化，如此一來就能夠完全克服過去推薦技術中所無法解決的問題，例如：新使用者之問題、新商品之問題、資料稀疏或資料量處理之執行效能的問題。經由實驗分析顯示，我們所提出的方法確實較現有其他方法在推薦技術上具有更高之準確度。

The explosive growth of information makes people confused in making a choice among a huge amount of products, like movies, books, etc. To help people clarify what they want easily, in this study, we proposed an intelligent recommendation approach that integrates collaborative information and content features to predict user preferences on the basis of rough-set theory. The contribution of this paper is that our proposed approach can completely solve the traditional problems occurring in recent studies, such as cold-star, first-rater, sparsity and scalability problems. The empirical evaluation results reveal that our proposed approach can reduce the gap between user’s interest and recommended items more effectively than other existing approaches in terms of accuracy of recommendations.

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