关键词: 时空众包, 兴趣型时空众包工人, 时空行为分析

Abstract:

Current allocation methods of spatiotemporal crowdsourcing task are mostly targeted at crowdsourcing workers who have incentive condition constraints and full-time crowdsourcing task, ignoring interest-oriented crowdsourcing workers who have interest preferences and fulfil tasks without reward constraint. The problem how to recommend crowdsourcing tasks to these interest-oriented workers needs to be solved urgently. In view of this, this method considers the spatiotemporal behavior rules and interest preferences of spatiotemporal crowdsourcing workers. This method firstly introduces the gini coefficient and selects the data of interest-oriented spatiotemporal crowdsourcing workers from the data. The clustering method of the geo-social relationship model is used to cluster the crowdsourcing tasks. The Markov model based on Gaussian analysis is used to predict the probability of various locations that the crowdsourcing workers may arrive at the next time point. The tasks are recommended to the interest-oriented workers in descending order of probability. Experimental results show that the proposed method effectively improves the completion rate of interest-oriented spatiotemporal crowdsourcing tasks.

Key words: spatiotemporal crowdsoourcing technology, interest-oriented spatiotemporal crowdsourcing workers, spatiotemporal behavior analysis