  An important aspect of a researcher's activities is to find relevant and
related publications. The task of a recommender system for scientific
publications is to provide a list of papers that match these criteria. Based on
the collection of publications managed by Mendeley, four data sets have been
assembled that reflect different aspects of relatedness. Each of these
relatedness scenarios reflect a user's search strategy. These scenarios are
public groups, venues, author publications and user libraries. The first three
of these data sets are being made publicly available for other researchers to
compare algorithms against. Three recommender systems have been implemented: a
collaborative filtering system; a content-based filtering system; and a hybrid
of these two systems. Results from testing demonstrate that collaborative
filtering slightly outperforms the content-based approach, but fails in some
scenarios. The hybrid system, that combines the two recommendation methods,
provides the best performance, achieving a precision of up to 70%. This
suggests that both techniques contribute complementary information in the
context of recommending scientific literature and different approaches suite
for different information needs.
