一种融合科研人员标签的学术论文推荐方法

Academic Paper Recommendation Method Combined with Researcher Tag

近年来,科研社交网络的兴起在一定程度上转变了科研人员原有的科研交流合作模式,深受科研人员的欢迎;然而,科研社交网络上激增的研究成果数量使得科研人员很难找到自己真正感兴趣的学术论文。因此,为科研人员推荐其感兴趣的学术论文,成为一项重要任务。考虑到科研社交网络中科研人员阅读论文数据的特殊性,文中从单类协同过滤角度考虑科研社交网络中的论文推荐问题。一方面,利用科研人员的标签信息进行更精确的负例抽取,并在此基础上考虑科研人员的活跃度以确定负例数量;另一方面,基于添加完负例的科研人员-学术论文评分矩阵进行概率矩阵分解,在概率矩阵分解阶段融合科研人员标签关联矩阵以及论文相似度信息来进行约束,以缓解数据稀疏对最终结果的不利影响。最后,在科研社交网络“科研之友”上进行实验,采用准确率、召回率、平均准确率、平均倒数排名这4项评价指标对推荐结果的准确性及推荐排序进行验证。实验结果表明,所提方法相较于主流方法取得了更好的结果,在准确率指标上提升了4.19%,验证了所提方法将论文推荐考虑为单类协同过滤问题的有效性,以及社会化信息对推荐的有效辅助作用;并且,所提方法在推荐系统中具有良好的可扩展性,能够在科研社交网络中为科研人员进行有效的论文推荐。

In recent years,the rise of scientific social networks has changed the original mode of exchanges and cooperation among researchers to some extent,which makes scientific social networks well received by researchers.With the surge of research fin-dings on scientific social networks,it’s difficult for researchers to find research papers they are really interested in.Consequently,it becomes an important task to recommend the papers that researchers are interested in.Considering the particularity of resear-chers’reading data,this paper conducted paper recommen-dation from the perspective of one class collaborative filtering.On the one hand,researchers’tag information is used to extract negative cases precisely;on the other hand,based on the researcher-paper matrix with negative instances incorporated,the researchers-tag matrix and papers’similarity information are jointly integrated into the probability matrix factorization,to alleviate the data sparsity problem.Finally,experiments were carried out on a scientific social network,ScholarMate.Four evaluation metrics,namely precision,recall,MAP,and MRR,were adopted to verify the recommendation accuracy as well as the recommendation order.The experimental results show that the proposed method performs better than the baselines with an improvement of 4.19%in terms of the precision,which demonstrate the effectiveness of considering the paper recommendation on scientific social networks as a one-class collaborative filtering problem,the effectiveness of introducing extra social information to improve the recommendation results,and the scalability of the proposed method.