許多學術會議期刊使用雙盲同行審查流程以確保審查的公平性。在雙盲同行審查流程中，學術論文的作者們與審查者們彼此都不知道對方的身分。然而，在雙盲同行審查流程中，作者的身分是否能有效地被隱藏，是一個有趣的研究題目。對此，我們提出一個偵測雙盲論文作者身分的問題，探討是否能從這些作者過往的著作中獲得資訊，進而辨識出雙盲論文作者的身分。

為了解決這個問題，首先我們從arXiv電子資料庫收集了許多論文。我們根據袋字模型從這些論文抽取特徵字來建造文章對字矩陣，以及作者對字矩陣。藉由使用這兩個矩陣，我們提出以下三種來自協同濾波以及內容倒向濾波的預測方法偵測作者: (1)餘弦相似度，(2)在二分圖上的隨機漫步，(3)矩陣因子分解。

在實驗部分，我們比較這三種方法的準確率。在實驗結果中可見到，餘弦相似度方法擁有最高的準確率94%。然而餘弦相似度方法的運算時間可能會過長，因此，我們提出了最小雜湊法來提升餘弦相似度方法的效率。在實驗結果中我們可見到，擁有二十篇以上過往著作的作者，其身分會有超過90%的機率被我們的系統偵測出來;也就是說，雙盲同行審查流程很難去隱藏那些擁有許多過往著作的作者們的身分。

Many conferences and journals use double-blind peer review to ensure the fairness of the review process. In double-blind peer review process, neither author nor reviewer identities are revealed. One interesting research question is to see whether the double-blind paper review process can indeed conceal the authors' identities.

For this, we consider an authors detection problem of double-blind papers to see whether the authors of the double-blind papers can be detected with the information of their past publications.
To solve the authors detection problem, we rst collect a large set of papers from arXiv. Based on the bag-of-word model, we parse these papers to extract terminologies
of authors to construct a document-term matrix and an author-term matrix. By using these matrices, we propose three prediction methods to detect the authors: (i)cosine
similarity, (ii)random walk on the bipartite graph, and (iii)matrix factorization, which are collaborative ltering and content-based ltering techniques.

We compare the accuracy of these three methods in our experiments. Experimental results show that the cosine similarity method has highest accuracy 94%. However, the
computation time of the cosine similarity method might be too long. Therefore, we use minhash to improve the efficiency of the cosine similarity method. We can see that
authors who wrote more than 20 papers have more than 90% probability of being detected by our system; in other words, it is difficult to conceal authors' identities of those who
published a lot of papers in the past.

Contents 1
List of Figures 2
1 Introduction 3
2 Feature extraction 5
3 Prediction Methods 7
3.1 Cosine Similarity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
3.1.1 MinHash . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
3.2 Random walk on the bipartite graph . . . . . . . . . . . . . . . . . . . . 9
3.2.1 Random walks with a xed number of steps . . . . . . . . . . . . 10
3.2.2 Random walks with a stopping probability . . . . . . . . . . . . . 10
3.3 Matrix factorization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
4 Experimental results 13
4.1 Experimental Setup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
4.1.1 Training Papers . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
4.1.2 Testing Papers . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
4.2 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
4.2.1 Cosine Similarity . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
4.2.2 Random walk on the bipartite graph . . . . . . . . . . . . . . . . 17
4.2.3 Matrix factorization . . . . . . . . . . . . . . . . . . . . . . . . . 17
4.2.4 Comparison . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
5 Conclusion 22

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