CSSR: A Context-Aware Sequential Software Service Recommendation Model

TL;DR

CSSR is a new context-aware sequential recommendation model for GitHub repositories that uses graph embeddings and deep learning to improve recommendation accuracy amidst data sparsity.

Contribution

It introduces a novel context-induced graph embedding method combined with sequential deep learning for software service recommendation.

Findings

Outperforms existing methods on GitHub dataset

Effectively alleviates data sparsity issues

Captures user preference dynamics

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.