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- W3216437276 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." @default.
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- W3216437276 date "2021-01-01" @default.
- W3216437276 modified "2023-10-14" @default.
- W3216437276 title "CSSR: A Context-Aware Sequential Software Service Recommendation Model" @default.
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- W3216437276 doi "https://doi.org/10.1007/978-3-030-91431-8_45" @default.
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