A Crowdsourcing Recommendation That Considers the Influence of Workers

In the context of the continuous development of the Internet, crowdsourcing has received continuous attention as a new cooperation model based on the relationship between enterprises, the public and society. Among them, a reasonably designed recommendation algorithm can recommend a batch of suitable workers for crowdsourcing tasks to improve the final task completion quality. Therefore, this paper proposes a crowdsourcing recommendation framework based on workers' influence (CRBI). This crowdsourcing framework completes the entire process design from task distribution, worker recommendation, and result return through processes such as worker behavior analysis, task characteristics construction, and cost optimization. In this paper, a calculation model of workers' influence characteristics based on the ablation method is designed to evaluate the comprehensive performance of workers. At the same time, the CRBI framework combines the traditional open-call task selection mode, builds a new task characteristics model by sensing the influence of the requesting worker and its task performance. In the end, accurate worker recommendation and task cost optimization are carried out by calculating model familiarity. In addition, for recommending workers to submit task answers, this paper also proposes an aggregation algorithm based on weighted influence to ensure the accuracy of task results. This paper conducts simulation experiments on some public datasets of AMT, and the experimental results show that the CRBI framework proposed in this paper has a high comprehensive performance. Moreover, CRBI has better usability, more in line with commercial needs, and can well reflect the wisdom of group intelligence.