Discovering users' interests is essential in order to help them explore resources in large digital repositories. In particular, correctly identifying users' interests is commonly a good approach for organising information and providing personalised recommendations. We consider here the case of discovering users' research interests in Mendeley a research platform for scholarly article management and discovery. Prior work in this area has considered approaches such as matrix factorisation and text-based topic modelling for inferring topics of interest in recommendation scenarios. These approaches present several problems, such as little or no interpretability of the inferred topics and difficulty handling similarities in vocabulary in different research disciplines. We present an effective solution for extracting coherent and interpretable research topics that leverages the reference management data in Mendeley in a three-step approach: 1) a topic model based on the interactions between users and articles rather than article content, 2) keyword extraction to label the topics using article titles and author-declared keywords and 3) identifying the research interests of users based on the articles that they have added to their libraries. An evaluation comprised of a research interest prediction task and an article recommendation task shows the validity of our proposal in different research disciplines (clearly outperforming a text-based latent topic model) and provides further insights regarding the effects of number of latent topics in the model and the trade-off between recency and quantity of the users' libraries.