Advances in social network sites enable researchers to access to large on-line archives of scientific articles. These social network sites allow researchers to create their personal libraries for the on-line articles that interest them and to share the libraries with other researchers. This makes recommender systems helpful for researchers to find interesting articles. In this paper, we propose topic regression matrix factorization models to recommend scientific articles for on-line community. The main idea of topic regression Matrix Factorization models lies in extending the matrix factorization with a probabilistic topic modeling. Further, we demonstrate the efficacy of topic regression Matrix Factorization models on a large subset of the data from CiteULike, a bibliography sharing service dataset.