Cold-start developer recommendation in software crowdsourcing: A topic sampling approach

Abstract

Recently, software crowdsourcing platforms, which provide paid tasks for developers, become attractive to both employers and developers. Developers expect to find tasks that match their interests and capabilities via crowdsourcing platforms, and thus recommender systems play important roles in these platforms. However, we still face several challenges when building a recommender system for a crowdsourcing platform. A major challenge is how to recommend tasks to cold-start developers whose task interaction data is not available. This paper presents a novel, topic sampling approach to tackling with the cold-start developer recommendation problem. First, it employs a general method for modeling developers and tasks, which solves the data heterogeneous issue across different platforms. After that, it casts the cold-start developer recommendation problem into a multi-optimization problem, and takes a topic-sampling based genetic algorithm to recommend tasks. More specifically, our approach is different from traditional solutions in that it leverages task descriptions and popularity-To-be, allowing new tasks to be recommended to cold-start developers. To evaluate the effectiveness of the proposed approach, we have conducted experiments on a large dataset crawled from three real-world software crowdsourcing platforms. Compared with other state-ofthe-Art recommendation solutions, the experimental results show that the proposed approach improves 75% of precision and recall on average.