  Citation recommendation systems for the scientific literature, to help
authors find papers that should be cited, have the potential to speed up
discoveries and uncover new routes for scientific exploration. We treat this
task as a ranking problem, which we tackle with a two-stage approach: candidate
generation followed by re-ranking. Within this framework, we adapt to the
scientific domain a proven combination based on "bag of words" retrieval
followed by re-scoring with a BERT model. We experimentally show the effects of
domain adaptation, both in terms of pretraining on in-domain data and
exploiting in-domain vocabulary. In addition, we introduce a novel
navigation-based document expansion strategy to enrich the candidate documents
processed by our neural models. On three different collections from different
scientific disciplines, we achieve the best-reported results in the citation
recommendation task.
