【摘要】 从海量科研数据中自动发现潜在合作者是科研合作预测研究的热点。鉴于学者研究兴趣随时间变化以及人们更倾向于与具有一定学术社交关系的学者合作，本文提出一种融合学者动态研究偏好和学术社交信任的潜在科研合作者推荐模型SimTrustRec。首先，利用LDA (latent Dirichlet allocation)模型学习已发表论文的主题分布，挖掘学者动态研究偏好特征，计算学者间研究偏好相似度；其次，根据论文中学者、单位共现关系构建学术社交网络，计算直接学术社交信任值，根据信任的传递性，计算间接学术社交信任值；最后，融合研究兴趣相似度和学术社交信任值计算学者间潜在合作的可能性，生成潜在合作者推荐列表。真实数据集ArnetMiner上的实证研究结果表明，相对于已有方法，本文方法在召回率、命中率、平均倒数排序方面均有一定的提升。更多还原

【Abstract】 Discovering potential collaborators automatically from massive data is a hot topic in scientific collaboration prediction. Considering that research interests change over time and people with social relationships are more likely to collaborate, a potential scientific collaborator recommendation model “SimTrustRec” is proposed, which integrates dynamic research interest and academic social trust. First, the Latent Dirichlet Allocation model is used to learn the topic distribution of published papers, and dynamic research interests of scholars are mined to calculate the similarity of research interests between two scholars. Second, an academic social network is constructed based on the co-occurrence relationship of scholars and units in papers. Direct academic social trust values are calculated and indirect academic social trust values are then calculated based on the transitivity of social trust. Finally, the possibility of potential collaboration between two scholars is calculated by combining research interest similarity and academic social trust value, and a list of potential collaborators is generated. Experimental results using ArnetMiner datasets demonstrate that the proposed method achieves better performance in terms of recall, hit rate, and mean reciprocal rank compared to existing methods.更多还原