论文标题
有效的混合尺寸嵌入用于基质分解的嵌入
Efficient Mixed Dimension Embeddings for Matrix Factorization
论文作者
论文摘要
尽管在推荐系统领域中神经网络方法突出，但由于几个原因，在行业中仍使用了简单的方法，例如矩阵分解和二次损失。这些模型可以通过交替的最小二乘训练，这使它们易于以众多并行的方式实现，从而使其可以利用来自现实世界数据集的数十亿个事件。大规模推荐系统需要考虑到用户和项目的分布中严重的流行，因此许多研究重点是实施稀疏，混合维度或共享嵌入，以减少参数数量和对稀有用户和物品的过度拟合。在本文中，我们提出了两个具有混合尺寸嵌入的矩阵分解模型，可以使用交替的最小二乘方法以大规模平行的方式进行优化。
Despite the prominence of neural network approaches in the field of recommender systems, simple methods such as matrix factorization with quadratic loss are still used in industry for several reasons. These models can be trained with alternating least squares, which makes them easy to implement in a massively parallel manner, thus making it possible to utilize billions of events from real-world datasets. Large-scale recommender systems need to account for severe popularity skew in the distributions of users and items, so a lot of research is focused on implementing sparse, mixed dimension or shared embeddings to reduce both the number of parameters and overfitting on rare users and items. In this paper we propose two matrix factorization models with mixed dimension embeddings, which can be optimized in a massively parallel fashion using the alternating least squares approach.
