CSSR: A Context-Aware Sequential Software Service Recommendation Model
This work addresses the challenge of finding suitable software services for GitHub users, representing an incremental advance by applying sequential recommendation techniques to this domain for the first time.
The authors tackled the problem of recommending GitHub repositories by proposing a model that uses context-aware graph embeddings to address data sparsity and sequential interactions to capture user preference dynamics, achieving superior results in experiments against existing methods.
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.