Bayesian Co-Clustering Truth Discovery for Mobile Crowd Sensing Systems

Abstract

With the proliferation of mobile devices, mobile crowd sensing (MCS) has emerged as a new data collection paradigm, which allows the crowd to act as sensors and contribute their observations about entities. Unfortunately, users with varied skills and motivations may provide conflicting information for the same entity. Existing work solves this problem by estimating user reliability and inferring the correct observations (i.e., truths). However, these methods assume that users’ expertise degrees are dependent on the truths, but ignore the finer clusters that exist even in the entities with the same truths. To capture users’ fine-grained reliability on different entity clusters, we propose a novel Bayesian co-clustering truth discovery model for the task of observation aggregation. This model enables us to produce a more precise estimation while taking into account the entity clusters and the user clusters. Experiments on four real-world datasets reveal that our method outperforms the state-of-the-art approaches in terms of accuracy and F1-score.