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.

中文翻译：

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HybridCite：上下文感知引文推荐的混合模型

引文推荐系统旨在为完整的论文或称为引文上下文的一小部分文本推荐引文。针对引文上下文推荐引文的过程称为局部引文推荐，是本文的重点。首先，我们开发了基于嵌入、主题建模和信息检索技术的引文推荐方法。据我们所知，我们第一次将性能最佳的算法组合成一个半遗传混合推荐系统，用于引文推荐。我们基于多个数据集离线评估单一方法和混合方法，例如 Microsoft Academic Graph (MAG) 和结合 arXiv 和 ACL 的 MAG。我们进一步进行了一项用户研究，以在线评估我们的方法。