research-article

Efficient Learning-Based Recommendation Algorithms for Top-N Tasks and Top-N Workers in Large-Scale Crowdsourcing Systems

Authors:

Dunren CheAuthors Info & Claims

ACM Transactions on Information Systems (TOIS), Volume 37, Issue 1

Article No.: 2, Pages 1 - 46

https://doi.org/10.1145/3231934

Published: 30 October 2018 Publication History

Abstract

The task and worker recommendation problems in crowdsourcing systems have brought up unique characteristics that are not present in traditional recommendation scenarios, i.e., the huge flow of tasks with short lifespans, the importance of workers’ capabilities, and the quality of the completed tasks. These unique features make traditional recommendation approaches no longer satisfactory for task and worker recommendation in crowdsourcing systems. In this article, we propose a two-tier data representation scheme (defining a worker--category suitability score and a worker--task attractiveness score) to support personalized task and worker recommendations. We also extend two optimization methods, namely least mean square error and Bayesian personalized rank, to better fit the characteristics of task/worker recommendation in crowdsourcing systems. We then integrate the proposed representation scheme and the extended optimization methods along with the two adapted popular learning models, i.e., matrix factorization and kNN, and result in two lines of top-N recommendation algorithms for crowdsourcing systems: (1) Top-N-Tasks recommendation algorithms for discovering the top-N most suitable tasks for a given worker and (2) Top-N-Workers recommendation algorithms for identifying the top-N best workers for a task requester. An extensive experimental study is conducted that validates the effectiveness and efficiency of a broad spectrum of algorithms, accompanied by our analysis and the insights gained.

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Index Terms

Efficient Learning-Based Recommendation Algorithms for Top-N Tasks and Top-N Workers in Large-Scale Crowdsourcing Systems
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Supervised learning
        Learning to rank
2. Information systems

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cover image ACM Transactions on Information Systems

ACM Transactions on Information Systems Volume 37, Issue 1

January 2019

435 pages

ISSN:1046-8188

EISSN:1558-2868

DOI:10.1145/3289475

Editor:
Maarten de Rijke
University of Amsterdam, The Netherlands

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Publication History

Published: 30 October 2018

Accepted: 01 June 2018

Revised: 01 April 2018

Received: 01 August 2017

Published in TOIS Volume 37, Issue 1

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