Corwdsourced Task Recommendation via Link Prediction

Mobile crowdsourcing (MCS) can solve problems that are difficult for computers to solve accurately or efficiently. Current crowdsourcing workers face the challenges of overload, task recommendation is presented to deal with the above issue. The existing MCS task recommendation methods only consider the workers themselves, without taking the potential relationships between workers and workers and tasks, which may fail to grasp the long-term behavioral preferences of workers and cannot solve the cold start problem efficiently. To solve the above problems, this paper proposes a task recommendation method based on heterogeneous graph link prediction. First, we try to obtain the connection relationships between crowdsourcing tasks and workers and build a worker-task heterogeneous graph accordingly. Then, majority voting and DeepWalk are used to generate initial node features of workers. Second, we design two efficient message passing mechanisms to aggregate and update node features between task and worker nodes to explore the potential relationships between nodes. Finally, we obtain attention weights among nodes and use Bi-GRU to capture the long-term behavioral preferences of workers and recommend appropriate tasks to workers based on the similarity between workers and tasks to improve perceptual quality. Evaluations are conducted on seven real datasets. Experimental results show that our method is superior to the state-of-the-art baselines.