Abstract: 为了解决众包任务推荐存在的数据稀疏问题和提高众包任务推荐的准确性，本文提出了一种基于协同知识图谱与混合神经网络的众包任务推荐方法。该方法首先利用任务实体对齐融合工人-任务二分图与众包任务知识图谱形成工人-任务协同知识图谱，来缓解数据稀疏性的问题；其次，采用双向门控循环单元编码工人和任务之间的多条路径，在考虑路径之间关联性的情况下利用注意力机制将编码多条路径得到的信息加权聚合，以更准确地学习工人的偏好，从而更加准确地的推荐众包任务；同时，采用图卷积网络捕捉众包任务间的高相关性来充分考虑实体复杂的语义信息；最后，根据得到的工人和任务的嵌入表示向工人综合推荐。在MovieLens-1M、Yelp、Book-Crossing、Music、Zhu-Bajie和CHI六个公开数据集的实验结果显示，与基准模型相比，本文所提方法在AUC指标方面平均提升了5.8%、7.85%、5.75%、6.3%、5.47%和4.58%，在其他指标方面本文模型也均有提高。实验结果证明了本文方法的有效性与稳定性，可以为众包任务推荐领域提供一个研究思路。

关键词: In order to solve the problem of data sparsity in crowdsourcing task recommendation and improve the accuracy of crowdsourcing task recommendation, this paper proposes a crowdsourcing task recommendation method based on collaborative knowledge graph and hybrid neural network. This method first utilizes task entity alignment to fuse the worker task bipartite graph and crowdsourcing task knowledge graph to form a worker task collaborative knowledge graph to alleviate the problem of data sparsity, Secondly, a bidirectional gated recurrent unit is used to encode multiple paths between workers and tasks. Considering the correlation between paths, attention mechanism is utilized to weight and aggregate the information obtained from encoding multiple paths, in order to more accurately learn workers' preferences and recommend crowdsourcing tasks more accurately, At the same time, graph convolutional networks are used to capture the high correlation between crowdsourcing tasks and fully consider the complex semantic information of entities, Finally, based on the embedded representations of the workers and tasks obtained, provide comprehensive recommendations to the workers. The experimental results on six publicly available datasets, MovieLens-1M, Yelp, Book Crossing, Music, Zhu Bajie, and CHI, showed that compared with the benchmark model, the method proposed in this paper improved the AUC index by an average of 5.8%, 7.85%, 5.75%, 6.3%, 5.47%, and 4.58%, and the model also improved in other indicators. The experimental results demonstrate the effectiveness and stability of the proposed method, which can provide a research approach for the field of crowdsourcing task recommendation.