MCS Recommendation Optimization for Electronic Equipment Based on HM and LWR Algorithms
In order to improve the data quality in Mobile Crowd Sensing(MCS),a recommendation method HM-LWR based on Hybrid Model(HM)and List-Wise Ranking(LWR)is designed.The results show that the optimal parameters are determined as learning rate μ is 0.01,iteration 100 times,α value 0.5.The HM-LWR al-gorithm model can accurately predict the task preferences of participants,and can effectively improve the accuracy and operation efficiency when assigning MCS tasks.This research is helpful to improve the mobile group perception ability of electronic devices,and has good promotion value in the field of smart city.
mobile crowd sensingtask recommendationcollaborative sortinghybrid modelparticipant in-tention
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