CSSR: A Context-Aware Sequential Software Service Recommendation Model

• URL: http://arxiv.org/abs/2112.10316v1
• Date: Mon, 20 Dec 2021 03:17:42 GMT
• Title: CSSR: A Context-Aware Sequential Software Service Recommendation Model
• Authors: Mingwei Zhang, Jiayuan Liu, Weipu Zhang, Ke Deng, Hai Dong, Ying Liu
• Abstract summary: 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. It then leverages sequence information of user-repository interactions for the first time in the software service recommendation field.
• Score: 4.306391411024746
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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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