  We propose a novel software service recommendation model to help users find
their suitable repositories in GitHub. Our model first designs a novel
context-induced repository graph embedding method to leverage rich contextual
information of repositories to alleviate the difficulties caused by the data
sparsity issue. It then leverages sequence information of user-repository
interactions for the first time in the software service recommendation field.
Specifically, a deep-learning based sequential recommendation technique is
adopted to capture the dynamics of user preferences. Comprehensive experiments
have been conducted on a large dataset collected from GitHub against a list of
existing methods. The results illustrate the superiority of our method in
various aspects.
