推薦系統為網站提升使用者滿意度、減少使用者所花費的時間並且替網站提供方提升銷售，是現在網站中不可或缺的要素，而推薦系統的研究集中在娛樂項目，學術研究論文推薦系統的研究有限。若能給予有價值的相關文獻，提供協助，無疑是加速進步的速度。 在過去的研究中，為了達到個人化目的所使用的方法，都有不可避免或未解決的缺點，2002年美國研究圖書館協會提出布達佩斯開放獲取計劃(Budapest Open Access Initiative)，不要求使用者註冊帳號與支付款項就能取得研究論文全文，這樣的做法使期刊走向開放的風氣開始盛行，時至今日，開放獲取對學術期刊網站帶來重大的影響。在這樣的時空背景之下，本研究提出一個適用於學術論文之推薦機制，以FP-Growth演算法與協同過濾做為推薦方法的基礎，消弭過去研究之缺點，並具個人化推薦的優點，經實驗驗證後，證實本研究所提出的推薦架構具有良好的成效。 / Recommendation system is used in many field like movie, music, electric commerce and library. It’s not only save customers’ time but also raise organizations’ efficient. Recommended system is an essential element in a website. Some methods have been developed for recommended system, but they are primarily focused on content or collaboration-based mechanisms. For academic research, it is very important that relevant literature can be provided to researchers when they conduct literature review. Previous research indicates that there are inevitable or unsolved shortcomings in existing methods such as cold starts. Association of Research Libraries purpose “Budapest Open Access Initiative” that is advocate open access concept. Open access means that users can get full paper without register and pay fee. It’s a major impact to academic journal website. In this space-time background, we propose a hybrid recommendation mechanism that takes into consideration the nature of recommendation academic papers to mitigate the shortcomings of existing methods.