TasRec

Abstract

Lately, crowdsourcing has emerged as a viable option of getting work done by leveraging the collective intelligence of the crowd. With many tasks posted every day, the size of crowdsourcing platforms is growing exponentially. Hence, workers face an important challenge of selecting the right task. Despite the task filtering criteria available on the platform to select the right task, crowd workers find it difficult to choose the most relevant task and must glean through the filtered tasks to find the relevant tasks. In this paper, we propose a framework for recommending tasks to workers. The proposed framework evaluates the worker's fitment over the tasks based on worker's preference, past tasks (s)he has performed, and tasks done by similar workers. We evaluated our approach on the datasets collected from popular crowdsourcing platform. Our experimental results based on 5,000 tasks and 3,000 workers show that the recommendation made by our framework is significantly better as compared to the baseline approach.