Willing and able: Task recommendation with a trade-off of the bilateral benefits for knowledge-intensive crowdsourcing

Abstract

Given the “profit-seeking” behavior of task solvers and the “quality-seeking” focus of solution seekers in knowledge-intensive crowdsourcing contests, task recommender systems must manage the trade-off between their respective benefits. This study proposes a multitask deep learning model with a multigate hybrid expert structure to jointly model solver preference and ability, thereby balancing bilateral benefits. The knowledge source for participation and performance prediction tasks are grounded in expectancy theory and performance theory, respectively. Linear and deep neural network (DNN) modules are integrated to enhance both memorization and generalization capabilities. By incorporating gating networks, the model effectively captures correlations between the two prediction tasks, balances intertask weights, and allows each task to learn features in different ways using linear and DNN modules. Additionally, our method addresses sample selection bias and data sparsity issues through feature transfer learning, leveraging the sequential pattern between participation and winning. Cross-validation experiments on Kaggle data demonstrate the model effectiveness, provide data-driven decision support for task recommendation and resource allocation in knowledge-intensive crowdsourcing platforms.