New Task Oriented Recommendation method Based on Hungarian algorithm in Crowdsourcing Platform

As a distributed problem-solving model based on human-machine integration, crowdsourcing has attracted wide attention in industry and academia with the development of Internet technology. There are many prominent problems on the crowdsourcing platform, for example, the task can't be noticed by users and the users are not competent for the task, resulting in huge waste of time and economy. If we can make coping recommendations according to the characteristics of the tasks, the operational efficiency of crowdsourcing platforms will be greatly improved. Therefore, this paper proposed a new task oriented recommendation method based on Hungarian algorithm in crowdsourcing platform. Aiming at the problem that the new users and tasks on the crowdsourcing platform have low matching degree, we establish the multi-objective optimization model of task recommendation with the aim of maximizing quality, time and cost efficiency. Then, the model is solved by the Hungarian algorithm through appropriate transformation. The experimental results show that the proposed method can improve the recommendations accuracy of new tasks, and therefor effectively improve the operational efficiency of crowdsourcing platforms.