Task Recommendation via Heterogeneous Multi-modal Features and Decision Fusion in Mobile Crowdsensing

Abstract

In the decision-making process of the behavior of mobile crowdsensing, using a single view to learn a user's preference will lead to a mismatch between the user's wishes and the final task recommendation list, resulting in the low efficiency of the model recommendation. Aiming at the lack of perceptual representation and cognitive fusion of multimodal coupled information, a task recommendation method based on heterogeneous multimodal features and decision fusion is proposed. According to the content characteristics of multi-source data in the user's historical task set, several task-task similarity matrices are constructed to align feature dimensions and feature semantics. Using the improved similarity network fusion algorithm, networks composed of multiple content similarity matrices are effectively fused into a similarity network. Considering the influence of the time factor, the tasks that have had interest drift are filtered out from the set of tasks that the user has participated in. Finally, the updated similarity network is clustered to predict the current preference of the user for new tasks. Experimental results based on simulation and real datasets show that the proposed method can effectively improve the accuracy and efficiency of task assignments while improving user satisfaction.