TA-GAE: Crowdsourcing Diverse Task Assignment Based on Graph Autoencoder in AIoT

With the recent development of AIoT (AI+IoT), crowdsourcing has emerged as a promising paradigm for distributed problem solving and business practice. Crowdsourcing entails posting tasks on a dedicated Web platform, enabling networked workers to choose preferred tasks on a first-come, first-served basis, typically of the same type to ensure high assignment accuracy. However, existing crowdsourcing task assignment methods do not take into account the potential fatigue of workers for similar tasks. In this article, we propose a task assignment architecture using a (TA-GAE), which comprehensively considers the relationship between the occupation and skills of workers and potential tasks, facilitating an accurate assignment of a wide variety of tasks to workers. The proposed architecture consists of three modules, The Graph Creation module analyzes the potential connections between tasks based on worker evaluations and constructs an initial task graph that represents these connections. The gravity-based graph autoencoder module is inspired by Newton’s law of universal gravitation. We analogize the tasks on the crowdsourcing platform to masses in the universe and calculate the mutual attractive force between two tasks to quantify their correlation. The Hybrid Task Assignment module recommends task lists to workers by combining traditional collaborative filtering and content-based task assignment strategies. The experimental results demonstrate that the proposed architecture outperforms several state-of-the-art methods and achieves a diversity rate of over 40% across four data sets: 1) fliggy trip; 2) MovieLens 1M; 3) library; and 4) survey.