Research And Implementation Of Crowd-based Collaborative Hybrid Incentive Mechanism Of Gamification

Crowdsourcing,as a mode of crowd-based collaborative computing,realizes the coordinated development of workers' intellectual resources and Internet technology,but there are still many problems to be solved in this technology.Among them,many workers are unwilling to participate in crowdsourcing tasks due to their personal circumstances,or have insufficient ability,inability to do their best,or deception when participating in collaborative tasks,resulting in a decrease in the quality of the results.Therefore,how to design a reasonable and effective incentive mechanism to attract workers to participate and improve the quality of task results is a hot and difficult research field in this field.The design of traditional incentive mechanisms is mostly based on giving workers monetary rewards and other external material needs to attract them to participate in crowdsourcing tasks.However,studies have shown that relying solely on material rewards cannot provide continuous incentives.To obtain high-quality task results,it is costly.The cost will increase substantially.In response to the above problems,the thesis studies the research progress and existing problems of the incentive mechanism of crowd-based collaborative at home and abroad in recent years.Starting from the participation of people in collaborative tasks,the paper conducts an in-depth analysis of the incentive methods of workers.Summarize and summarize,and take the worker's intrinsic motivation as the main research point,to study the feasibility and incentive effect of gamification for group intelligence collaborative task incentives,and the realization method of gamification characteristics in practical application systems.The thesis has achieved innovation in the realization and quantitative evaluation of multi-factor mixed incentive mechanism,and the self-generation method of crowdsourcing collaborative iterative tasks,and achieved the following research results:(1)A hierarchical classification diagram of the crowd-based collaborative incentive mechanism is proposed.Based on the research and analysis of the crowd-based collaborative incentive mechanism at home and abroad in recent years,a hierarchical classification diagram of the crowd-based collaborative incentive mechanism is proposed to provide theoretical support for the design and research of the incentive mechanism based on worker participation motivation;(2)Propose a gamified hybrid incentive mechanism based on collaborative tasks.Based on the flow theory,introduce gamification incentive elements,study the combination of gamification elements and the quantitative evaluation method of incentive effects under the internal and external incentive mechanism,design and complete a multi-factor mixed incentive mechanism that integrates gamification elements to continue to increase Worker participation;(3)A self-iterative task generation method for crowdsourcing translation is designed.Taking Sino-Laos collaborative translation as an example,research and propose a content self-generation method for crowdsourced translation iterative tasks,using the method of self-generation of mixed-question task content,to solve the problem of high repetition rate and rising cost of randomly selected task content;(4)A gamified mixed incentive system with bilingual translation tasks as an example is realized.Combining the research results of the gamification model and designing gamification elements,a self-iterative system of crowdsourcing translation based on gamification mixed incentives was completed to motivate workers to continue to participate,optimize their work effects,and ensure the quality of task completion.Finally,complete the result analysis and comprehensive quantitative experimental evaluation of the incentive effect.Through comparative experiments,it is proved that by applying the hybrid incentive mechanism in the system,the participation of co-workers in tasks and the quality of completed tasks are significantly improved.