High-Dimensional and Incomplete matrices, which usually contain a large amount of valuable latent information, can be well represented by a Latent Factor Analysis model. The performance of an LFA model heavily rely on its optimization process. Thereby, some prior studies employ the Particle Swarm Optimization to enhance an LFA model's optimization process. However, the particles within the swarm follow the static evolution paths and only share the global best information, which limits the particles' searching area to cause sub-optimum issue. To address this issue, this paper proposes a Dynamic-neighbor-cooperated Hierarchical PSO-enhanced LFA model with two-fold main ideas. First is the neighbor-cooperated strategy, which enhances the randomly chosen neighbor's velocity for particles' evolution. Second is the dynamic hyper-parameter tunning. Extensive experiments on two benchmark datasets are conducted to evaluate the proposed DHPL model. The results substantiate that DHPL achieves a higher accuracy without hyper-parameters tunning than the existing PSO-incorporated LFA models in representing an HDI matrix.

翻译：高维非完整矩阵通常包含大量有价值的潜在信息，可通过潜在因子分析模型得到良好表征。LFA模型的性能高度依赖于其优化过程。为此，部分先前研究采用粒子群优化（PSO）来增强LFA模型的优化过程。然而，群内粒子遵循静态演化路径且仅共享全局最优信息，这限制了粒子的搜索区域并导致次优问题。针对该问题，本文提出一种动态邻域协作分层PSO增强LFA模型，其核心思想包含两点：第一是邻域协作策略，通过增强随机选取邻域的粒子速度来驱动粒子演化；第二是动态超参数调优。通过在两个基准数据集上的广泛实验评估了所提出的DHPL模型。实验结果表明，与现有结合PSO的LFA模型相比，DHPL无需超参数调优即可在表征HDI矩阵时达到更高精度。