Optimizing Participant Allocation for Multicontest Success: A Heterogeneous Temporal Graph Learning Approach

Crowdsourcing contests have become an important approach for organizations to tackle complex problems by gathering innovative solutions from globally distributed participants. However, as platforms always host an increasing number of concurrent contests, the voluntary and selective nature of participants usually leads to coordination difficulties and unpredictable outcomes, which present a critical challenge to guide multicontest settings toward success. To address this issue, this article proposes a novel and effective dynamic link prediction-based recommendation algorithm tailored for crowdsourcing platforms named the heterogeneous temporal graph generative adversarial network (HTGGAN). The HTGGAN leverages participants’ historical interaction data to predict the optimal set of participants for each future task. This ensures that participants’ skills and interests align with the contest requirements, thereby improving the success of crowdsourcing contests. Specifically, the HTGGAN contains four modules. First, the metapath-relation aggregation (MRA) module integrates information from multiple metapaths of the heterogeneous temporal graph (HTG) into a unified spatial embedding. Second, the group-relation aggregation (GRA) module incorporates learnable group-level features into node-level representation learning to enhance the expressiveness of the target node. Third, the across-time aggregation (ATA) module captures interactions between the target node and its temporal neighbors, enabling the learning of an initial spatiotemporal embedding. Finally, the optimized recommendation (OR) module incorporates a generative adversarial network and performs link prediction to recommend appropriate users for competitions or posts. Using a large real-world dataset constructed from the Kaggle platform, we demonstrate that HTGGAN outperforms several state-of-the-art algorithms across multiple metrics and delivers clear practical value.