GitHub has become a popular social application platform, where a large number
of users post their open source projects. In particular, an increasing number
of researchers release repositories of source code related to their research
papers in order to attract more people to follow their work. Motivated by this
trend, we describe a novel item-item cross-platform recommender system,
paper2repo, that recommends relevant repositories on GitHub that
match a given paper in an academic search system such as Microsoft Academic.
The key challenge is to identify the similarity between an input paper and its
related repositories across the two platforms, without the benefit of human labeling. Towards that end, paper2repo integrates text encoding and
constrained graph convolutional networks (GCN) to automatically learn and map
the embeddings of papers and repositories into the same space, where proximity
offers the basis for recommendation. To make our method more practical in real
life systems, labels used for model training are computed automatically from
features of user actions on GitHub. In machine learning, such automatic
labeling is often called {\em distant supervision\/}. To the authors'
knowledge, this is the first distant-supervised cross-platform (paper to
repository) matching system. We evaluate the performance of paper2repo on
real-world data sets collected from GitHub and Microsoft Academic. Results
demonstrate that it outperforms other state of the art recommendation methods