论文标题
混合晶格：一种用于上下文感知引文建议的混合模型
HybridCite: A Hybrid Model for Context-Aware Citation Recommendation
论文作者
论文摘要
引用推荐系统的目的是为完整纸张或一小部分称为引文上下文的文本推荐引用。推荐引用引用环境的过程称为本地引文建议，是本文的重点。首先，我们根据嵌入，主题建模和信息检索技术开发引用推荐方法。据我们所知，我们首次将表现最佳的算法结合在一起，成为一种半遗传混合建议系统进行引用建议。我们根据几个数据集评估了单个方法和混合方法离线，例如Microsoft Academic Graph（MAG）和MAG与ARXIV和ACL结合使用。我们进一步进行了一项用户研究，以在线评估我们的方法。我们的评估结果表明，包含嵌入和信息检索组件的混合模型优于其各个组件，并以很大的边距更高的算法。
Citation recommendation systems aim to recommend citations for either a complete paper or a small portion of text called a citation context. The process of recommending citations for citation contexts is called local citation recommendation and is the focus of this paper. Firstly, we develop citation recommendation approaches based on embeddings, topic modeling, and information retrieval techniques. We combine, for the first time to the best of our knowledge, the best-performing algorithms into a semi-genetic hybrid recommender system for citation recommendation. We evaluate the single approaches and the hybrid approach offline based on several data sets, such as the Microsoft Academic Graph (MAG) and the MAG in combination with arXiv and ACL. We further conduct a user study for evaluating our approaches online. Our evaluation results show that a hybrid model containing embedding and information retrieval-based components outperforms its individual components and further algorithms by a large margin.
