Scientific article recommender systems are playing an increasingly important role for researchers in retrieving scientific articles of interest in the coming era of big scholarly data. Most existing studies have designed unified methods for all target researchers and hence the same algorithms are run to generate recommendations for all researchers no matter which situations they are in. However, different researchers may have their own features and there might be corresponding methods for them resulting in better recommendations. In this paper, we propose a novel recommendation method which incorporates information on common author relations between articles (i.e., two articles with the same author(s)). The rationale underlying our method is that researchers often search articles published by the same author(s). Since not all researchers have such author-based search patterns, we present two features, which are defined based on information about pairwise articles with common author relations and frequently appeared authors, to determine target researchers for recommendation. Extensive experiments we performed on a real-world dataset demonstrate that the defined features are effective to determine relevant target researchers and the proposed method generates more accurate recommendations for relevant researchers when compared to a Baseline method.

中文翻译：

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科学文章推荐：利用共同作者关系和历史偏好

在即将到来的大学术数据时代，科学文章推荐系统在检索感兴趣的科学文章方面对研究人员发挥着越来越重要的作用。现有的大多数研究都为所有目标研究人员设计了统一的方法，因此无论研究人员处于何种情况，都运行相同的算法为所有研究人员生成建议。 然而，不同的研究人员可能有自己的特点，可能会有相应的方法产生在更好的建议中。在本文中，我们提出了一种新的推荐方法，该方法结合了文章之间共同作者关系的信息（即，具有相同作者的两篇文章）。我们方法的基本原理是研究人员经常搜索同一作者发表的文章。由于并非所有研究人员都具有这种基于作者的搜索模式，因此我们提出了两个特征，这些特征是根据具有共同作者关系的成对文章和经常出现的作者的信息定义的，以确定推荐的目标研究人员。我们在真实世界数据集上进行的大量实验表明，定义的特征可有效确定相关目标研究人员，并且与基线方法相比，所提出的方法可为相关研究人员生成更准确的建议。