Task Recommendation with Developer Social Network in Software Crowdsourcing

Recently, crowdsourcing has been increasingly used in software industry to lower costs and increase innovations, by utilizing experiences, labor, or creativity of developers worldwide. In software crowdsourcing platforms, developers expect to find suitable tasks for their interests and abilities. So it is significant for software crowdsourcing to build a recommender system to match developers with suitable tasks. However, there are a significant number of inactive developers who have very sparse historical behavior records in the platform, and thus state-of-the-art recommendation approaches in software crowdsourcing, such as collaborative filtering, suffer from this cold-start problem. In this paper, a social influence-based method is proposed to recommend suitable tasks for both active and inactive developers. The essential idea of the novel method is (1) to construct developer social network from developer behaviors, such as browsing and bidding for tasks, (2) to calculate the influence degrees between developers using developer social network, (3) to recommend tasks to active developers using SiSVD, and (4) to recommend tasks to inactive developers by combining the recommended tasks of their friends. We have evaluated our method on a large real data set from the JointForce, a popular software crowdsourcing platform in China. The results show that our method is feasible and practical for recommendation in software crowdsourcing. In particular, the F1-Measure of our method for inactive developers with task-bidding friends is increased by 16.7% than other previous approaches averagely.