Cai Xiaoyan, Han Junwei, Li Wenjie, Zhang Renxian, Pan Shirui, Yang Libin
IEEE Trans Neural Netw Learn Syst. 2018 Dec;29(12):6026-6037. doi: 10.1109/TNNLS.2018.2817245. Epub 2018 Apr 12.
Fast-growing scientific papers pose the problem of rapidly and accurately finding a list of reference papers for a given manuscript. Citation recommendation is an indispensable technique to overcome this obstacle. In this paper, we propose a citation recommendation approach via mutual reinforcement on a three-layered graph, in which each paper, author or venue is represented as a vertex in the paper layer, author layer, and venue layer, respectively. For personalized recommendation, we initiate the random walk separately for each query researcher. However, this has a high computational complexity due to the large graph size. To solve this problem, we apply a three-layered interactive clustering approach to cluster related vertices in the graph. Personalized citation recommendations are then made on the subgraph, generated by the clusters associated with each researcher's needs. When evaluated on the ACL anthology network, DBLP, and CiteSeer ML data sets, the performance of our proposed model-based citation recommendation approach is comparable with that of other state-of-the-art citation recommendation approaches. The results also demonstrate that the personalized recommendation approach is more effective than the nonpersonalized recommendation approach.
快速增长的科学论文带来了一个问题，即要快速、准确地为给定的手稿找到参考文献列表。引用推荐是克服这一障碍必不可少的技术。在本文中，我们提出了一种基于三层图上的相互强化的引用推荐方法，其中每篇论文、作者或期刊分别在论文层、作者层和期刊层中表示为一个顶点。对于个性化推荐，我们针对每个查询研究人员分别启动随机游走。然而，由于图规模较大，这具有很高的计算复杂度。为了解决这个问题，我们应用一种三层交互式聚类方法对图中的相关顶点进行聚类。然后在由与每个研究人员需求相关联的聚类生成的子图上进行个性化引用推荐。在ACL文集网络、DBLP和CiteSeer机器学习数据集上进行评估时，我们提出的基于模型的引用推荐方法的性能与其他一些最先进的引用推荐方法相当。结果还表明，个性化推荐方法比非个性化推荐方法更有效。
