Digging out the latent information from large-scale incomplete matrices is a key issue with challenges. The Latent Factor Analysis (LFA) model has been investigated in depth to an alyze the latent information. Recently, Swarm Intelligence-related LFA models have been proposed and adopted widely to improve the optimization process of LFA with high efficiency, i.e., the Particle Swarm Optimization (PSO)-LFA model. However, the hyper-parameters of the PSO-LFA model have to tune manually, which is inconvenient for widely adoption and limits the learning rate as a fixed value. To address this issue, we propose an Adam-enhanced Hierarchical PSO-LFA model, which refines the latent factors with a sequential Adam-adjusting hyper-parameters PSO algorithm. First, we design the Adam incremental vector for a particle and construct the Adam-enhanced evolution process for particles. Second, we refine all the latent factors of the target matrix sequentially with our proposed Adam-enhanced PSO's process. The experimental results on four real datasets demonstrate that our proposed model achieves higher prediction accuracy with its peers.

翻译：从大规模不完整矩阵中挖掘潜在信息是一个具有挑战性的关键问题。潜在因子分析（LFA）模型已被深入研究以分析潜在信息。近年来，基于群体智能的LFA模型被提出并广泛采用，以高效优化LFA过程，即粒子群优化（PSO）-LFA模型。然而，PSO-LFA模型的超参数需要手动调整，这不利于广泛采用，且将学习率限制为固定值。为解决此问题，我们提出了一种Adam增强分层PSO-LFA模型，该模型通过顺序Adam自适应超参数PSO算法来优化潜在因子。首先，我们设计了粒子的Adam增量向量，并构建了粒子Adam增强的演化过程。其次，我们利用所提出的Adam增强PSO过程顺序优化目标矩阵的所有潜在因子。在四个真实数据集上的实验结果表明，与同类模型相比，我们提出的模型实现了更高的预测精度。