隨著網路世代的興起，資訊量大幅的增加，現今推薦系統已不單只使用於電子商務，各個領域都有這方面的需求。在學術領域，隨著文獻電子化的普及以及文獻數量的大幅增增加，已有許多幫助尋找文獻的學術搜尋服務的需求出現，論文推薦已是一個重要的課題。在論文推薦系統中，因為普遍相關文獻的收集方式可能造成大量的候選集，使得尋找潛在相關論文的難度相對提高。我們提出了一個關聯性共同作者的方法來幫助我們過濾出潛在的關聯文獻，並且結合傳統的引用文網絡，計算引用權威擴散程度，實驗結果證明我們的系統對於找關聯性論文有所幫助。

Now, recommender systems are not only used in e-commerce but in the field of scholarship, in every discipline. In academia, as more and more papers are published, it is becoming increasingly difficult to quickly find needed paper. Therefore, scholarly search engines and many assessment methods have begun to appear. Given that the paper recommendation issue is important today. In paper recommendation, because an excessive number of candidate set, there are some potential relevant papers cannot be recommended effectively in existing methods. We propose a Related Paper with Common Author method, which can effectively filter search results to provide potential answers without any abstracts or full text. We also propose a traditional citation network method, Citation Authority Propagation, which is a recommending method that uses authority author propagation. The experiment results demonstrate the effectiveness of the proposed system.

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