动态构建多相似度图，提升推荐系统个性化能力
Dynamic Graph with Similarity-Aware Attention Graph Neural Network for Recommender Systems
- 用四种相似度构建用户图，训练中动态更新
- 召回率16.2%，优于LightGCN基线
- 适合需要高精度推荐的电商平台
推荐系统是现代在线平台的核心，用于提供个性化内容。传统协同过滤依赖静态用户-物品交互图和有限的相似度度量，难以捕捉个体偏好的动态变化。现有基于图神经网络的方法虽关注用户-物品二分图，但缺乏显式用户间关系建模及训练过程中的图结构演化。本文提出动态图相似度感知注意力GNN（DG-SA-GNN）框架，融合动态用户相似度图构建、多相似度传播与注意力聚合。该架构采用余弦、杰卡德、折扣皮尔逊相关系数（Discount PCC）、IPIJ四种相似度函数并行构建四张用户相似度图，每张图由专用UserGNN模块处理；图变压器融合四视图信息，交叉注意力模块通过与物品嵌入交互优化用户表示。关键在于训练中定期重构图结构，使模型能适应学习到的嵌入空间，实现动态图机制。采用小批量硬负样本采样提升可扩展性与收敛速度。在MovieLens100K数据集上的实验表明，DG-SA-GNN达到Recall@20为0.162，NDCG@20为0.065，召回率优于LightGCN基线，验证了动态多相似度图构建与注意力融合对推荐性能的提升作用。
原文摘要 · Abstract (English)
Recommender systems are essential components of modern online platforms which presents personalized content in various domain. The traditional collaborative filtering methods depends on static user-item interaction graphs and a limited subset of similarity measures which fail to capture the changing nature of preferences of an individual. Recent graph neural network (GNN) based approaches focus on user-item bipartite graphs which do not use explicit user-user relational modelling and dynamic graph evolution during training. To address these limitations, this paper proposes a Dynamic Graph SimilarityAware Attention Graph Neural Network (DG-SA-GNN) framework that integrates dynamic user similarity graph construction with multi-similarity propagation and attention-based aggregation. The proposed architecture constructs four parallel user similarity graphs using Cosine, Jaccard, Discounted Pearson Correlation Coefficient (Discount PCC), and IPIJ similarity functions, each processed by a dedicated UserGNN module. A Graph Transformer fuses the four graph views, and a CrossAttention module refines user embeddings through interaction with item embeddings. Crucially, the graphs are reconstructed at scheduled epochs during training, enabling the model to adapt to the learned embedding space constituting the dynamic graph component. Mini-batch training with hard negative sampling improves scalability and convergence. Experiments on the MovieLens100K benchmark demonstrate that DG-SA-GNN achieves a Recall@20 of 0.162 and NDCG@20 of 0.065 which is better than the LightGCN baseline in recall. The results validate that dynamic multi-similarity graph construction coupled with attention-based fusion which produce recommendation performance
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。