Research paper recommenders emerged over the last decade to ease finding publications relating to
researchers’ area of interest. The challenge was not just to provide researchers with very rich publications at
any time, any place and in any form but to also offer the right publication to the right researcher in the right
way. Several approaches exist in handling paper recommender systems. However, these approaches
assumed the availability of the whole contents of the recommending papers to be freely accessible, which is
not always true due to factors such as copyright restrictions. This paper presents a collaborative approach for
research paper recommender system. By leveraging the advantages of collab- orative filtering approach, we
utilize the publicly available contextual metadata to infer the hidden associations that exist between
research papers in order to personalize recommen- dations. The novelty of our proposed approach is that it
provides personalized recommen- dations regardless of the research field and regardless of the user’s
expertise. Using a publicly available dataset, our proposed approach has recorded a significant improvement
over other baseline methods in measuring both the overall performance and the ability to return relevant and
useful publications at the top of the recommendation list.
A Scalable Hybrid Research Paper Recommender System for Microsoft Academic
