Collaborative filtering (CF) is widely searched in recommendation with various types of solutions. Recent success of Graph Convolution Networks (GCN) in CF demonstrates the effectiveness of modeling high-order relationships through graphs, while repetitive graph convolution and iterative batch optimization limit their efficiency. Instead, item similarity models attempt to construct direct relationships through efficient interaction encoding. Despite their great performance, the growing item numbers result in quadratic growth in similarity modeling process, posing critical scalability problems. In this paper, we investigate the graph sampling strategy adopted in latest GCN model for efficiency improving, and identify the potential item group structure in the sampled graph. Based on this, we propose a novel item similarity model which introduces graph partitioning to restrict the item similarity modeling within each partition. Specifically, we show that the spectral information of the original graph is well in preserving global-level information. Then, it is added to fine-tune local item similarities with a new data augmentation strategy acted as partition-aware prior knowledge, jointly to cope with the information loss brought by partitioning. Experiments carried out on 4 datasets show that the proposed model outperforms state-of-the-art GCN models with 10x speed-up and item similarity models with 95\% parameter storage savings.

翻译：协同过滤（CF）在推荐系统中被广泛研究，并衍生出多种解决方案。图卷积网络（GCN）在CF中的成功证明了通过图建模高阶关系的有效性，然而重复的图卷积操作与迭代批优化限制了其效率。相比之下，物品相似度模型试图通过高效交互编码构建直接关系。尽管性能优异，但物品数量的增长会导致相似度建模过程中的二次增长，带来严重的可扩展性问题。本文研究了最新GCN模型中用于提升效率的图采样策略，并识别出采样图中潜在的分组结构。基于此，我们提出一种新型物品相似度模型，该模型引入图划分，将物品相似度建模限制在每个分区内。具体而言，我们证明原始图的谱信息能很好地保留全局信息。随后，将其作为分区感知先验知识，通过一种新数据增强策略对局部物品相似度进行微调，从而共同应对划分带来的信息损失。在4个数据集上的实验表明，所提模型以10倍速度提升和95%参数存储节省的优势，优于现有最优的GCN模型与物品相似度模型。