  Finding online research papers relevant to one's interests is very
challenging due to the increasing number of publications. Therefore,
personalized research paper recommendation has become a significant and timely
research topic. Collaborative filtering is a successful recommendation
approach, which exploits the ratings given to items by users as a source of
information for learning to make accurate recommendations. However, the ratings
are often very sparse as in the research paper domain, due to the huge number
of publications growing every year. Therefore, more attention has been drawn to
hybrid methods that consider both ratings and content information.
Nevertheless, most of the hybrid recommendation approaches that are based on
text embedding have utilized bag-of-words techniques, which ignore word order
and semantic meaning. In this paper, we propose a hybrid approach that
leverages deep semantic representation of research papers based on social tags
assigned by users. The experimental evaluation is performed on CiteULike, a
real and publicly available dataset. The obtained findings show that the
proposed model is effective in recommending research papers even when the
rating data is very sparse.
