An optimized task assignment framework based on crowdsourcing knowledge graph and prediction

Abstract

In spatial crowdsourcing, both workers and tasks are added to the system on the fly, making it a real-time platform with complex circumstances. Most research has focused on optimizing the total number of assigned tasks, with little attention paid to factors such as worker preferences and task completion rate in a practical environment. To capture the complex spatiotemporal correlation and the latent supply–demand relationship, this work proposes a convolutional spatiotemporal attention model (Conv-STAN) and a cluster-based time-weighted voting method (CT-Voting) for predicting the future distribution of crowdsourcing entities. We develop a crowdsourcing knowledge graph (CKG) that reflects the semantic linkages between entities and provide an innovative and effective state representation approach (CKG2vec) for quantifying the preference to demonstrate the impact of a dynamically changing environment on worker preferences. In addition, a local subgraph incremental updating approach (LSG-IncrUpdate) is constructed to simulate the interaction between various entities in a crowdsourcing setup. Finally, we introduce a reinforcement learning framework (CKG-RLTA) that integrates prediction and the crowdsourcing knowledge graph to conduct task assignment. The experimental findings show that our approach is sensitive to workers’ preferences and improves task completion rate, even when those preferences are subject to change.