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.

本文提出了一种名为 SAC 的新型框架，它采用了一种新的空间自回归范式来充分利用邻居的接近性和高阶信息，同时还提出了邻居信息瓶颈来学习目标节点到邻居的预测任务的最小充分表示，并消除邻居的冗余，实验结果表明，在公共推荐数据集和某真实情境下的大型数据集 Douyin-Friend-Recommendation 上，SAC 方法优于现有的最先进的方法。