We propose a new approach to recommend scientific literature, a domain in which the efficient
organization and search of information is crucial. The proposed system relies on the
hypothesis that two scientific articles are semantically related if they are co-cited more frequently
than they would be by pure chance. This relationship can be quantified by the probability
of co-citation, obtained from a null model that statistically defines what we consider
pure chance. Looking for article pairs that minimize this probability, the system is able to
recommend a ranking of articles in response to a given article. This system is included in
the co-occurrence paradigm of the field. More specifically, it is based on co-cites so it can
produce recommendations more focused on relatedness than on similarity. Evaluation has
been performed on the ACL Anthology collection and on the DBLP dataset, and a new
corpus has been compiled to evaluate the capacity of the proposal to find relationships
beyond similarity. Results show that the system is able to provide, not only articles similar
to the submitted one, but also articles presenting other kind of relations, thus providing
diversity, i.e. connections to new topics.