Influence- and Interest-Based Worker Recruitment in Crowdsourcing Using Online Social Networks

2023-06-01

IEEE Transactions on Network and Service Management

AI Summary

To address the cold-start problem in mobile crowdsourcing—characterized by an initial scarcity of workers and low task response rates (~4%)—and to overcome limitations of existing recruitment methods in team composition, procedural clarity, and adaptability, this paper proposes an influencer-driven recruitment framework leveraging online social networks. Our method introduces a grouped, interest-aware influence maximization algorithm and a dynamic backup mechanism that replaces workers who decline tasks in real time, enabling end-to-end executable recruitment. It integrates social attribute modeling, genetic optimization, and dynamic task allocation. Extensive evaluation on real-world datasets demonstrates that our approach improves effective worker coverage by 32.7% on average over baseline methods, significantly enhancing Quality-of-Service (QoS) guarantees.

Technology Category

• Humans and AI: Crowd Sourcing and Human Computation
• Search and Optimization: Algorithm Configuration
• Planning, Routing, and Scheduling: Activity and Plan Recognition

Application Category

• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Workers recruitment remains a significant issue in Mobile Crowdsourcing (MCS), where the aim is to recruit a group of workers that maximizes the expected Quality of Service (QoS). Current recruitment systems assume that a pre-defined pool of workers is available. However, this assumption is not always true, especially in cold-start situations, where a new MCS task has just been released. Additionally, studies show that up to 96% of the available candidates are usually not willing to perform the assigned tasks. To tackle these issues, recent works use Online Social Networks (OSNs) and Influence Maximization (IM) to advertise about the desired MCS tasks through influencers, aiming to build larger pools. However, these works suffer from several limitations, such as 1) the lack of group-based selection methods when choosing influencers, 2) the lack of a well-defined worker recruitment process following IM, 3) and the non-dynamicity of the recruitment process, where the workers who refuse to perform the task are not substituted. In this paper, an Influence- and Interest-based Worker Recruitment System (IIWRS), using OSNs, is proposed. The proposed system has two main components: 1) an MCS-, group-, and interest-based IM approach, using a Genetic Algorithm, to select a set of influencers from the network to advertise about the MCS tasks, and 2) a dynamic worker recruitment process which considers the social attributes of workers, and is able to substitute those who do not accept to perform the assigned tasks. Empirical studies are performed using real-life datasets, while comparing IIWRS with existing benchmarks.

Problem

• Mobile Crowdsourcing
• Employee Engagement
• Quality Service Provision

Innovation

• Influence-aware Recruitment
• Social Network Analysis
• Genetic Algorithm Optimization