不依赖训练参数的图卷积重排序，让推荐系统更快更准。
Non-parametric Graph Convolution for Re-ranking in Recommendation Systems
- 测试时动态使用图卷积，避开训练阶段的计算瓶颈。
- 平均提升8.1%的推荐效果，额外开销仅0.5个单位。
- 可直接嵌入现有系统，适合追求高效高质推荐的团队。
图知识在推荐系统中已被证明能有效提升物品排序质量，尤其是在检索阶段。然而，在拥有更丰富用户-物品交互上下文信息的排序阶段应用图方法仍研究不足。主要挑战在于：从分布式系统中反复检索数十亿物品的邻域信息带来巨大计算开销，使图方法难以在实际推荐系统中扩展。本文首先证明在排序阶段引入图结构可显著提升排序质量。但同时发现，图卷积带来的计算成本在真实场景中难以承受。为此，我们提出一种非参数化策略：仅在测试时使用图卷积进行重排序。该策略避免了训练阶段的图卷积计算开销，实时利用图中隐藏的结构信息。作为即插即用模块，可轻松增强多种排名层的性能，且计算开销极低。在四个不同稀疏度的基准数据集上进行的全面实验表明，该方法在测试阶段平均提升8.1%，额外计算开销仅为0.5（单位）。代码已开源。
原文摘要 · Abstract (English)
Graph knowledge has been proven effective in enhancing item rankings in recommender systems (RecSys), particularly during the retrieval stage. However, its application in the ranking stage, especially when richer contextual information in user-item interactions is available, remains underexplored. A major challenge lies in the substantial computational cost associated with repeatedly retrieving neighborhood information from billions of items stored in distributed systems. This resource-intensive requirement makes it difficult to scale graph-based methods in practical RecSys. To bridge this gap, we first demonstrate that incorporating graphs in the ranking stage improves ranking qualities. Notably, while the improvement is evident, we show that the substantial computational overheads entailed by graphs are prohibitively expensive for real-world recommendations. In light of this, we propose a non-parametric strategy that utilizes graph convolution for re-ranking only during test time. Our strategy circumvents the notorious computational overheads from graph convolution during training, and utilizes structural knowledge hidden in graphs on-the-fly during testing. It can be used as a plug-and-play module and easily employed to enhance the ranking ability of various ranking layers of a real-world RecSys with significantly reduced computational overhead. Through comprehensive experiments across four benchmark datasets with varying levels of sparsity, we demonstrate that our strategy yields noticeable improvements (i.e., 8.1% on average) during testing time with little to no additional computational overheads (i.e., 0.5 on average). Code: https://github.com/zyouyang/RecSys2025_NonParamGC.git
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