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.

本研究探讨了建议系统中协同过滤、图卷积网络、物品相似性模型、图采样策略和图分区等方面，并提出了一种使用新的数据增强策略的新型物品相似性模型以提高效率。实验结果表明，该模型在性能上优于现有的 GCN 模型和物品相似性模型。