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Software crowdsourcing has become a cornerstone of the Internet economy because of its unique capacity for selecting optimal workers to complete specific tasks. However, new workers face limited task opportunities compared to experienced workers, which negatively impacts their motivation and decreases overall activity on crowdsourcing platforms. This reduced activity can harm platform reputation. To encourage the active participation of new workers, this study introduces a novel method to identify and match worker–task preferences. Our approach categorizes preferred tasks based on golden tasks, historical data, and worker interests. We then present the Multi-Objective Worker–Task Recommendation (MOWTR) algorithm, built upon the Non-Dominated Sorting Genetic Algorithm II (NSGA-II). The MOWTR algorithm allocates tasks by considering worker preferences, salaries, and capabilities, aiming to optimize collective team performance while minimizing team costs, especially for new workers. New crossover and two-stage mutation operators are incorporated to increase algorithm efficiency. Experimental evaluations on four real and synthetic datasets demonstrate that MOWTR outperforms four advanced baseline methods, confirming its effectiveness.

The task allocation process in the Witkey mode is dynamic and open, in which Witkey is a rational person. Due to Witkey's individual rationality, in order to make the result of task allocation stable, task allocation must reach the Nash equilibrium. However, the Nash equilibrium point does not necessarily have the highest total system revenue. In order to make the task allocation result stable and have a high total system revenue, this paper proposes an incentive measure based on integral ranking to improve user participation. When Witkey adopts the best response strategy to select task, the order of participating in the selection will affect the individual income of Witkey to a certain extent. The higher the order, the greater the probability of obtaining higher system income. Based on this idea, a dynamic task allocation algorithm with complex tasks in the Witkey mode is proposed by combining incentive measures with best response strategy and reasonable benefit allocation strategy. The finally simulation results verified the effectiveness of the proposed algorithm, and the impact of incentive measures on the total revenue of the system was also examined.