research-article

Tasks Recommendation in Crowdsourcing based on Workers' Implicit Profiles and Performance History

Authors:

Menna Maged Kamel,

Alberto Gil-Solla,

Manuel Ramos-CarberAuthors Info & Claims

ICSIE '20: Proceedings of the 9th International Conference on Software and Information Engineering

Pages 51 - 55

https://doi.org/10.1145/3436829.3436834

Published: 05 January 2021 Publication History

Abstract

Crowdsourcing allows to build online platforms that make use of the power of human intelligence to complete tasks that are difficult to tackle for current algorithms. Current approaches to crowdsourcing adopt a methodology where tasks are published on specialized web platforms to a group of networked workers who can pick their preferred tasks freely on a first-come-first-served basis. Although this approach has several advantages, however it doesn't consider workers differences and capabilities. With the vast number of tasks posted by the requesters every day it's a challenging issue to satisfy both workers and requesters. In this paper, a crowdsourcing recommendation approach is proposed and evaluated that is based on a push methodology. This method aims to help workers to instantly find best matching tasks according to their interests and qualifications as well help the requesters to pick from the crowd the best workers for their desired tasks.

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Cited By

Yang GWei KXie GWu JHe XLiu Y(2024)Potential Dependency Analysis Based Task Recommendation Model for CrowdsourcingIEEE Transactions on Network Science and Engineering10.1109/TNSE.2024.338344011:4(3718-3730)Online publication date: Jul-2024
https://doi.org/10.1109/TNSE.2024.3383440
Wang GCheng DXia DJiang H(2023)Swarm Intelligence Research: From Bio-inspired Single-population Swarm Intelligence to Human-machine Hybrid Swarm IntelligenceMachine Intelligence Research10.1007/s11633-022-1367-720:1(121-144)Online publication date: 10-Jan-2023
https://doi.org/10.1007/s11633-022-1367-7
Farina MGorb AKruglov ASucci G(2022)Technologies for GQM-Based Metrics Recommender Systems: A Systematic Literature ReviewIEEE Access10.1109/ACCESS.2022.315239710(23098-23111)Online publication date: 2022
https://doi.org/10.1109/ACCESS.2022.3152397
Show More Cited By

Index Terms

Tasks Recommendation in Crowdsourcing based on Workers' Implicit Profiles and Performance History
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems
  2. World Wide Web
    1. Web applications
      1. Crowdsourcing

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ICSIE '20: Proceedings of the 9th International Conference on Software and Information Engineering

November 2020

251 pages

ISBN:9781450377218

DOI:10.1145/3436829

Copyright © 2020 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Ain Shams University: Ain Shams University, Egypt

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Association for Computing Machinery

New York, NY, United States

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Published: 05 January 2021

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ICSIE 2020

ICSIE 2020: 2020 9th International Conference on Software and Information Engineering

November 11 - 13, 2020

Cairo, Egypt

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Cited By

Yang GWei KXie GWu JHe XLiu Y(2024)Potential Dependency Analysis Based Task Recommendation Model for CrowdsourcingIEEE Transactions on Network Science and Engineering10.1109/TNSE.2024.338344011:4(3718-3730)Online publication date: Jul-2024
https://doi.org/10.1109/TNSE.2024.3383440
Wang GCheng DXia DJiang H(2023)Swarm Intelligence Research: From Bio-inspired Single-population Swarm Intelligence to Human-machine Hybrid Swarm IntelligenceMachine Intelligence Research10.1007/s11633-022-1367-720:1(121-144)Online publication date: 10-Jan-2023
https://doi.org/10.1007/s11633-022-1367-7
Farina MGorb AKruglov ASucci G(2022)Technologies for GQM-Based Metrics Recommender Systems: A Systematic Literature ReviewIEEE Access10.1109/ACCESS.2022.315239710(23098-23111)Online publication date: 2022
https://doi.org/10.1109/ACCESS.2022.3152397
Rozas DSaldivar JZelickson E(2021)The platform belongs to those who work on it! Co-designing worker-centric task distribution modelsProceedings of the 17th International Symposium on Open Collaboration10.1145/3479986.3479987(1-12)Online publication date: 15-Sep-2021
https://dl.acm.org/doi/10.1145/3479986.3479987

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