论文标题
通过分布式粒子群优化的实用二阶潜在因子模型
A Practical Second-order Latent Factor Model via Distributed Particle Swarm Optimization
论文作者
论文摘要
潜在因子（LF）模型可有效地通过低级矩阵近似来表示高维和稀疏（HID）数据。 Hessian无（HF）优化是利用LF模型目标函数的二阶信息的有效方法，并已用于优化二阶LF（SLF）模型。但是，SLF模型的低级表示能力在很大程度上取决于其多个超参数。确定这些超参数是耗时的，它在很大程度上降低了SLF模型的实用性。为了解决这个问题，在这项工作中提出了实用的SLF（PSLF）模型。它通过分布式粒子群优化器（DPSO）实现了超参数自适应，该粒子群（DPSO）无梯度且并行化。对真实HID数据集的实验表明，PSLF模型比最新模型在数据表示能力中具有竞争优势。
Latent Factor (LF) models are effective in representing high-dimension and sparse (HiDS) data via low-rank matrices approximation. Hessian-free (HF) optimization is an efficient method to utilizing second-order information of an LF model's objective function and it has been utilized to optimize second-order LF (SLF) model. However, the low-rank representation ability of a SLF model heavily relies on its multiple hyperparameters. Determining these hyperparameters is time-consuming and it largely reduces the practicability of an SLF model. To address this issue, a practical SLF (PSLF) model is proposed in this work. It realizes hyperparameter self-adaptation with a distributed particle swarm optimizer (DPSO), which is gradient-free and parallelized. Experiments on real HiDS data sets indicate that PSLF model has a competitive advantage over state-of-the-art models in data representation ability.
