我們的研究，希望能幫助作者在撰寫論文時，得到合適的引用資料，除了考慮本文之外，還加入標題、摘要、關鍵字等論文結構資訊；並利用Wordnet改良傳統TF-IDF遇到文字量不足時資料疏散的問題；最後加入完整度門檻來調整不同權重對推薦結果的影響，至終從作者的角度給予合適的推薦文章。;The great amount of research results brings researchers recurrent difficulties. Searching for the appropriate citations become an extremely labor-intensive work. However the current study about paper citation recommendation almost recommend from the perspective of keywords or the articles. These methods can not recommend from the perspective of contexts. Even if there are some context-based methods, they didn’t take into account the information completeness of these contexts.
In this paper, we hope to help the authors who is writing papers during their research, getting the appropriate citation. In addition to considering the context of articles, but also the structure information of research papers like title, abstract, keywords and so on. We use Wordnet to improve the text information inadequate problem of traditional TF-IDF method. Finally, we take completeness threshold into consideration to adjust the problems of information incompleteness. Using this algorithm, we can recommend the appropriate citation in terms of authors eventually