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

Xiuya Liu, Tianzhang Xing, Xianjia Meng, Chase Q. Wu

Research output: Contribution to journalArticlepeer-review

Abstract

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 paper, we propose a task assignment architecture using a gravity-based graph autoencoder(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 crowd-sourcing 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 datasets: Fliggy Trip, MovieLens 1M, Library and Survey.

Original languageEnglish (US)
Pages (from-to)1
Number of pages1
JournalIEEE Internet of Things Journal
DOIs
StateAccepted/In press - 2023

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Information Systems
  • Hardware and Architecture
  • Computer Science Applications
  • Computer Networks and Communications

Keywords

  • Artificial intelligence
  • Computer architecture
  • Correlation
  • crowdsourcing
  • Crowdsourcing
  • Internet of Things
  • potential tasks
  • Surveys
  • Task analysis
  • Task assignment
  • worker occupation
  • worker skills

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