Efficient learning-based recommendation algorithms for top-N tasks and top-N workers in large-scale crowdsourcing systems

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 (defning 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 ft 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 efciency of a broad spectrum of algorithms, accompanied by our analysis and the insights gained.