Open source repository recommendation in social coding
Journal
SIGIR 2017 - Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval
Pages
1173-1176
Date Issued
2017
Author(s)
Abstract
Social coding and open source repositories have become more and more popular. Software developers have various alternatives to contribute themselves to the communities and collaborate with others. However, nowadays there is no effective recommender suggesting developers appropriate repositories in both the academia and the industry. Although existing one-class collaborative filtering (OCCF) approaches can be applied to this problem, they do not consider particular constraints of social coding such as the programming languages, which, to some extent, associate the repositories with the developers. The aim of this paper is to investigate the feasibility of leveraging user programming language preference to improve the performance of OCCF-based repository recommendation. Based on matrix factorization, we propose language-regularized matrix factorization (LRMF), which is regularized by the relationships between user programming language preferences. Extensive experiments have been conducted on the real-world dataset of GitHub. The results demonstrate that our framework significantly outperforms five competitive baselines.
Type
conference paper