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Citation recommendation systems aim to recommend citations for either acomplete paper or a small portion of text called a citation context. Theprocess of recommending citations for citation contexts is called localcitation recommendation and is the focus of this paper. Firstly, we developcitation recommendation approaches based on embeddings, topic modeling, andinformation retrieval techniques. We combine, for the first time to the best ofour knowledge, the best-performing algorithms into a semi-genetic hybridrecommender system for citation recommendation. We evaluate the singleapproaches and the hybrid approach offline based on several data sets, such asthe Microsoft Academic Graph (MAG) and the MAG in combination with arXiv andACL. We further conduct a user study for evaluating our approaches online. Ourevaluation results show that a hybrid model containing embedding andinformation retrieval-based components outperforms its individual componentsand further algorithms by a large margin.

HybridCite: A Hybrid Model for Context-Aware Citation Recommendation