融合文本主题的异构网络学术刊物推荐方法

Article Topic-Based Heterogeneous Information Network for Venue Recommendation

学术大数据的高速膨胀为学术工作者高效选择有效学术信息带来了巨大挑战,运用学术刊物推荐以应对学术信息过载是主流方式之一。此研究专门解决如何为论文手稿高效推荐合适投稿期刊这一问题。引入学术异构信息网络,融合论文文本主题信息,提出一种新的学术刊物推荐方法(SCVR)。借助主题模型建模论文摘要和标题等文本内容提取主题信息,指导不同类型节点映射到多主题特征空间;将元路径上下文信息聚合到目标节点,形成了多跳元路径下节点的多主题表示;将不同元路径下形成的节点向量进行融合,实现每个节点多元元路径下的多主题表示。SCVR利用节点文本内容和网络结构学习节点多主题表示,完成学术刊物推荐。在两个真实学术数据集上的测试发现,提出了一种基于异构信息网络且融合文本主题信息的学术刊物推荐方法,在相同条件下,SCVR的推荐效果比仅基于异构信息网络的推荐结果在Precision和NDCG上平均提高了2.7%,且比经典学术刊物推荐方法平均高了19%,说明SCVR在学术刊物推荐领域有更优良的性能。

s and topics by LDA and guides different types of nodes to map to the multi-topic feature space.Then,the meta-path contextual information is aggregated to the target node,forming a multi-topic node representation.Finally,the node representations from multiple mate-paths are combined into the final multi-topic node representations.SCVR learns the multi-topic node representations with paper content and network structure to venue recommendation.Experiments on two real academic datasets show that a heterogeneous information network recommendation incorporating article topics can effectively improve the performance of the venue recommendation.Compared with the current heterogeneous information network recommendation and traditional venue recommendation,the performance of SCVR has improved by an average of 2.7% and 19%,which indicates that SCVR has better performance in the area of venue recommendation.