An Extensible Bounded Rationality-Based Task Recommendation Scheme for From-Scratch Mobile Crowdsensing

Abstract

Mobile crowdsensing (MCS) has recently shown good performance in solving large-scale sensing tasks. As an essential topic in MCS, recommending tasks to participants has received extensive attention from researchers. Most studies assume that participants are absolutely rational, which is unrealistic because it is difficult for participants to know all the information about the transaction. Furthermore, most of them do not consider how to learn the preferences of new participants. In addition, their works are difficult to extend to different MCS scenarios. Considering the above problems, we propose an extensible bounded rationality-based task recommendation scheme (EBRTR), which contains a task recommendation framework and a bounded rationality decision-making model. First, a task recommendation framework that can be easily extended to various MCS scenarios is designed. Second, in our bounded rationality decision-making model, for participants with historical task information, according to the implicit information in their historical tasks, the human thinking mode with bounded rationality is simulated, and the improved classification and regression tree (ICART) algorithm is designed to construct the decision tree. For participants who newly join the platform, social information is introduced to construct an initial decision tree. Finally, extensive experimental evaluations demonstrate the effectiveness of the proposed scheme.