Research On Pricing Mechanism For Both Supplier And Demander In Crowdsourcing Market

With the rapid development of Internet,the combination of outsourcing and Internet has produced an overturning mode for labor cooperation – crowdsourcing.In the environment of crowdsourcing market,how to set an "appropriate" price to recruit workers to complete a given task at a reasonable quality and cost is a key problem which restricts the development of it.For the two typical pricing modes in the current crowdsourcing market,namely the Server(demander)Assigned Tasks(SAT)and the Worker(supplier)Selected Tasks(WST),the existing pricing methods mainly have the following problems: haven't consider the relationship between current pricing and historical transactions;due to the lack of standard data set,there were existing problems of smaller data volume and latitudes,incomplete pricing mechanism and larger pricing errors;model is simple,disunity and not consistent with the actual crowdsourcing situation.For these problems,this paper carries out the following research work.For SAT mode,this paper adopts the general steps of data mining for research,acquires the data of real crowdsourcing platform by Python spider and processes it into a standard data set.On this basis,a posted pricing mechanism based on Random Forests(RF)and Text Clustering(TC)method for crowdsourcing tasks is proposed.Experiments show that RF has better basic pricing effect than other machine learning methods,its optimal performance can be achieved after adjusting parameters and optimization,and adding TC can extract more fine-grained feature expressions between tasks,so as to further improve the pricing accuracy.For WST mode,this paper proposes a collaborative crowdsourcing pricing mechanism based on VCG auction and quality all-payment,comprehensively analyzes the online crowdsourcing scenarios,designs a general model and corresponding pricing algorithm based on relevant economic theories(reverse auction,all-pay auction,incentive compatibility,etc.),and proves that it can achieves the effect of higher algorithm efficiency,higher task completion quality,reasonable balance the benefits between employers and workers,and ensuring the truthful biding of workers.