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arXiv · 2112.10316

CSSR: A Context-Aware Sequential Software Service Recommendation Model

Mingwei Zhang·Jiayuan Liu·Weipu Zhang·Ke Deng·Hai Dong·Ying Liu
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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.

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Mingwei Zhang, Jiayuan Liu, Weipu Zhang, Ke Deng, Hai Dong, Ying Liu. 2021-12-20. CSSR: A Context-Aware Sequential Software Service Recommendation Model. https://doi.org/10.1007/978-3-030-91431-8_45

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