Recommender systems typically operate on high-dimensional sparse user-item matrices. Matrix completion is a very challenging task to predict one's interest based on millions of other users having each seen a small subset of thousands of items. We propose a Global-Local Kernel-based matrix completion framework, named GLocal-K, that aims to generalise and represent a high-dimensional sparse user-item matrix entry into a low dimensional space with a small number of important features. Our GLocal-K can be divided into two major stages. First, we pre-train an auto encoder with the local kernelised weight matrix, which transforms the data from one space into the feature space by using a 2d-RBF kernel. Then, the pre-trained auto encoder is fine-tuned with the rating matrix, produced by a convolution-based global kernel, which captures the characteristics of each item. We apply our GLocal-K model under the extreme low-resource setting, which includes only a user-item rating matrix, with no side information. Our model outperforms the state-of-the-art baselines on three collaborative filtering benchmarks: ML-100K, ML-1M, and Douban.
翻译:矩阵完成是一项非常艰巨的任务,要预测一个人的兴趣,因为有数百万其他用户各自都看到一小批数千件物品。我们提议了一个全球-本地内核基基矩阵完成框架,名为GLial-K,目的是概括并代表一个高维分散用户-项目基体进入一个具有少数重要特征的低维空间。我们的GLial-K可以分为两个主要阶段。首先,我们预先将一个自动编码器与本地内嵌式重量矩阵连接起来,该矩阵将数据从一个空间转换为地貌空间,使用2d-RBF内核。然后,预先训练的自动编码器与一个基于连动的全球内核生成的评级矩阵进行微调,该矩阵将反映每个项目的特性。我们在极低资源环境下应用我们的GLial-K模型,该模型仅包括一个用户-项目评级矩阵,没有侧面信息。我们的模型在三个协作过滤基准上超越了州-Ba-M的基线、M-100-M和M-M-M-M-M-M-M-M-M-M-M-M-L。