Graph embedding methods including traditional shallow models and deep Graph Neural Networks (GNNs) have led to promising applications in recommendation. Nevertheless, shallow models especially random-walk-based algorithms fail to adequately exploit neighbor proximity in sampled subgraphs or sequences due to their optimization paradigm. GNN-based algorithms suffer from the insufficient utilization of high-order information and easily cause over-smoothing problems when stacking too much layers, which may deteriorate the recommendations of low-degree (long-tail) items, limiting the expressiveness and scalability. In this paper, we propose a novel framework SAC, namely Spatial Autoregressive Coding, to solve the above problems in a unified way. To adequately leverage neighbor proximity and high-order information, we design a novel spatial autoregressive paradigm. Specifically, we first randomly mask multi-hop neighbors and embed the target node by integrating all other surrounding neighbors with an explicit multi-hop attention. Then we reinforce the model to learn a neighbor-predictive coding for the target node by contrasting the coding and the masked neighbors' embedding, equipped with a new hard negative sampling strategy. To learn the minimal sufficient representation for the target-to-neighbor prediction task and remove the redundancy of neighbors, we devise Neighbor Information Bottleneck by maximizing the mutual information between target predictive coding and the masked neighbors' embedding, and simultaneously constraining those between the coding and surrounding neighbors' embedding. Experimental results on both public recommendation datasets and a real scenario web-scale dataset Douyin-Friend-Recommendation demonstrate the superiority of SAC compared with state-of-the-art methods.

翻译：包含传统浅度模型和深图形神经网络(GNNs)的嵌入方法,包括传统浅度模型和深深图形神经网络(GNNs)等嵌入方法,已导致在建议中产生有希望的应用程序。然而,浅度模型,特别是随机行进算法,由于优化模式,未能充分利用抽样子子集或序列中的邻居近距离。基于GNN的算法,因为其优化范例,无法充分利用高端信息;在堆积过多层时,很容易造成超声波问题,这可能会使低度(长尾尾)项目的建议恶化,限制表达性和缩放能力。在本文中,我们提出一个新的 SAC 框架, 即空间自动递缩缩缩式算法, 以统一的方式解决上述问题。为了充分利用邻居近距离近距离和高排序信息, 我们设计了一个全新的空间自动递增模式。我们首先随机地遮住多点邻居, 嵌入目标节点, 将所有周围邻居的低度(长尾部)项目的建议整合成一个邻居的掩体,, 和隐藏的缩缩缩缩缩缩缩缩缩缩缩缩缩缩缩缩缩缩缩缩标签,, 并安装为我们用新的软床图图, 学习一个软化的软化的软化的软化的软化的软化的软化的软化的软化的软化的缩缩缩缩缩缩化的缩化的缩缩缩化的软化的软化的缩化的缩化的缩化的缩化的缩化的缩化的缩化的缩化的缩略图图。