推薦系統在日常生活中處處可見，諸如電子商務、線上購物、數位學習等等。推薦系統不僅提供身處在資訊爆炸時代的我們在選擇事物參考指標，也提升了在大量資料裡搜尋相關資訊的能力。近年來，推薦系統的發展已引起各領域學者的興趣與關注，在各種不同領域的大量數位內容中取得對貼近所求，也是多數研究鑽研的目標。
本研究之目的在於提出一套適用於各種領域的個人化推薦機制，結合本體論的技術可使得應用領域更有彈性。由於在語意網路上定義的共通格式使得系統能理解不同範疇的資料語意。從使用者過去對某些項目的評分，系統可分析出使用者的喜好，而透過不同領域定義好的語意，系統可以推論出在這眾多資料中還有哪些是使用者也會有興趣的項目。因此在本研究中，除了找出相關項目的推薦機制是研究重點之外，對使用者的喜好分析也是探索的目標之一。從使用者過去對選擇項目的評分，推論出使用者對於某些分類的關注值與喜好值，進而產生出推薦名單以供使用者做決策參考。
本研究主要採用的方式是以使用者過去評分記錄為分析對象，分析得之使用者對該領域的各分類關切度，並以基於知識系統的推薦技術為基準，透過本體論定義的屬性序列尋找相關項目，並在尋得項目時以使用者關切度計算出對該項目的喜好度。而在個人化模組訓練方面則是採用基因演算法使得架構模式具有學習能力，在訓練的過程中試圖找出最符合該使用者的參數值，藉以達到個人化的最終結果。

Recommender systems have changed the way we live for they provide strategies that help users search or make decisions within the overwhelming information spaces nowadays. They have played an important role in some area such as e-commerce and e-learning.
In this paper we propose a hybrid recommendation strategy of content-based and knowledge-based method which is flexible for any field to apply. By analyzing the past rating records of every user, the system learns the user’s preferences. After acquiring users’ preferences the semantic searching and discovery procedure takes place starting from high rated item. For every found item the system evaluates the Interest Intensity indicating to what degree the user might like it. Last but not the least, a personalized estimating module is trained using genetic algorithm for each user individually, and the personalized estimating model helps improve the precision of the estimated scores.
With the recommendation strategies and personalization strategies, users may have better recommendation results that are closer to their preferences.

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