  The success of software crowdsourcing depends on active and trustworthy pool
of worker supply. The uncertainty of crowd workers' behaviors makes it
challenging to predict workers' success and plan accordingly. In a competitive
crowdsourcing marketplace, competition for success over shared tasks adds
another layer of uncertainty in crowd workers' decision-making process.
Preliminary analysis on software worker behaviors reveals an alarming task
dropping rate of 82.9%. These factors lead to the need for an automated
recommendation system for CSD workers to improve the visibility and
predictability of their success in the competition. To that end, this paper
proposes a collaborative recommendation system for crowd workers. The proposed
recommendation system method uses five input metrics based on workers'
collaboration history in the pool, workers' preferences in taking tasks in
terms of monetary prize and duration, workers' specialty, and workers'
proficiency. The proposed method then recommends the most suitable tasks for a
worker to compete on based on workers' probability of success in the task.
Experimental results on 260 active crowd workers demonstrate that just
following the top three success probabilities of task recommendations, workers
can achieve success up to 86%
