Finding relevant scientific articles and collaborators is a time-consuming and challenging task in today's information-rich environment. Despite this challenge, the study and development of recommendation systems, based on the authors' collaboration network, productivity and area of research, as topics of interest, have not been practically deployed in healthcare organizations. To address this known practice gap and to promote collaboration, Schosy was developed. This system collects publication metadata from PubMed, as the data source, and combining Collaborative and ContentBased Filtering techniques coupled with the Latent Dirichlet Allocation Topic Modeling algorithm, it recommends collaborators based on the authors' work, collaboration among the authors, Medical Subject Headings (MeSH) terms and the productivity of relevant researchers. As a result, this system provides an interpretable latent structure for collaborators and biomedical databases in order to enhance the experience of finding collaboration, for and by researchers and non-technical users.