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The depth and breadth of research now being published is overwhelming for an individual researcher to keep track of, let alone consume. Recommender systems have been developed to make it easier for researchers to discover relevant content. However, these have predominately taken the form of item-to-item recommendations using citation network features or text similarity features. This paper details how the Mendeley Suggest recommender system has been designed and developed. We show how implicit user feedback (based on activity data from the reference manager) and collaborative filtering (CF) are used to generate the recommendations for Mendeley Suggest. Because collaborative filtering suffers from the cold start problem (the inability to serve recommendations to new users), we developed additional recommendation methods based on user-defined attributes, such as discipline and research interests. Our off-line evaluation shows that where possible, recommendations based on collaborative filtering perform best, followed by recommendations based on recent activity. However, for cold users (for whom collaborative filtering was not possible) recommendations based on discipline performed best. Additionally, when we segmented users by career stages, we found that among senior academics, content-based recommendations from recent activity had comparable performance to collaborative filtering. This justifies our approach of developing a variety of recommendation methods, in order to serve a range of users across the academic spectrum.