Task Publication Time Recommendation in Spatial Crowdsourcing

The increasing proliferation of networked and geo-positioned mobile devices brings about increased opportunities for Spatial Crowdsourcing (SC), which aims to enable effective location-based task assignment. We propose and study a novel SC framework, namely Task Assignment with Task Publication Time Recommendation. The framework consists of two phases, task publication time recommendation and task assignment. More specifically, the task publication time recommendation phase hybrids different learning models to recommend the suitable publication time for each task to ensure the timely task assignment and completion while reducing the waiting time of the task requester at the SC platform. We use a cross-graph neural network to learn the representations of task requesters by integrating the obtained representations from two semantic spaces and utilize the self-attention mechanism to learn the representations of task-publishing sequences from multiple perspectives. Then a fully connected layer is used to predict suitable task publication time based on the obtained representations. In the task assignment phase, we propose a greedy and a minimum cost maximum flow algorithm to achieve the efficient and the optimal task assignment, respectively. An extensive empirical study demonstrates the effectiveness and efficiency of our framework.