  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.
