Personalized item recommendation has become an essential target of Web applications, but it suffers from the efficiency problem due to a large volume of data. In particular, feature-based factorization machine models are generally limited by the vast number of feature dimensions, leading to catastrophic computation time. In this paper, we propose a Fast Discrete Factorization Machine (FDFM) method to resolve these issues by applying the hash coding technologies to factorization machine models. Specifically, it discretizes the real-valued feature vectors in the parameter model during the process of learning personalized item rankings, whereby the overall computational time can be greatly reduced. Besides, we propose convergence update rules to optimize the quantization loss of the binarization problem, which can be used in personalized ranking scenarios efficiently. Based on the evaluation in two real-world datasets, our proposed approach consistently shows better performance than other baselines, especially when using shorter binary codes.

个性化的项目推荐已成为Web应用程序的基本目标，但是由于数据量大，它存在效率问题。特别是，基于特征的分解机器模型通常受到大量特征尺寸的限制，从而导致灾难性的计算时间。在本文中，我们提出了一种快速离散因子分解机（FDFM）方法通过将哈希编码技术应用于分解机模型来解决这些问题。具体地说，它在学习个性化项目等级的过程中离散化参数模型中的实值特征向量，从而可以大大减少整体计算时间。此外，我们提出了收敛更新规则以优化二值化问题的量化损失，可以有效地用于个性化排名方案中。基于对两个实际数据集中的评估，我们提出的方法始终显示出比其他基准更好的性能，尤其是在使用较短的二进制代码时。