在網路快速發展和電子資訊爆炸的時代中，如何繁雜的資訊從中篩選資訊，滿足使用者的需求，是每個服務平台面臨的難題。而一個好的推薦系統，不僅協助使用者快速找到所需資訊，透過良好的購物體驗，網站可以有效地留住客戶。
本篇論文提出了一個根據使用者的個人偏好結合專家經驗的推薦系統，此系統使用了多標準決策中的層級分析法以及原始認知網路過程演算法。該系統並且不會因為新產品剛剛出現，沒有過去的歷史評分而影響該產品的推薦排序。此外，新創建的網站沒有過去的使用者評分數據，也可以使用本系統來提供適當的推薦結果。在此系統的輔助下，一般使用者只要專注於表達個人偏好，系統會自動與專家經驗整合，推薦適合使用者的產品。
我們將此系統應用在競爭激烈的租屋市場中，可以解決一般網站推薦結果不符合使用者個人偏好的問題以及評分數據不足的問題，還能深入分析使用者的喜好，預測使用者對商品的喜好程度，輔助使用者選擇符合其個人偏好的房屋。依據驗證結果可以發現，原始認知網路過程演算法推薦的相似度較高，較能符合使用者的期待。

With the repaid network development and information explosion, how to help users search information in huge amount of data becomes a services challenge. A good recommender system helps users quickly find the information they need, and the e-commerce platform can effectively retain customers through a good shopping experience.
This paper proposes a recommender system based on the user's personal preferences combined with expert experience. This system uses Analytic Hierarchy Process and Primitive Cognitive Network Process, which are popular multi-criteria decision-making algorithms. This system provides appropriate recommendation results without other users' rating data. With the assistance of this system, a general user expresses personal preferences, the system can automatically integrate with expert experience and then recommend products suitable for the user.
We apply this system to the fiercely competitive rental market. This system can solve the problem that the recommendation results do not meet the user's personal preferences. It can also deeply analyze user preferences, predict user behavior, and assist users in choosing houses which meet their personal preferences. According to the verification results, we can find that Primitive Cognitive Network Process can provide a higher accuracy compared to Analytic Hierarchy Process.

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