CSSR: A Context-Aware Sequential Software Service Recommendation Model
arXiv:2112.10316 · doi:10.1007/978-3-030-91431-8_45
Abstract
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.
16 pages, 5 figures, 2 tables, The long version of the paper with the same title in ICSoC 2021