基于邻域采样的多任务图推荐算法

Multi-Task Graph Recommendation Algorithm Based on Neighborhood Sampling

近年来,图神经网络(GNN)成为解决协同过滤的主流方法之一。它通过构建用户-物品图,模拟用户与物品的交互关系,并用GNN学习它们的特征表示。尽管现有在模型结构上的研究已取得了较大进展,但如何在图结构上更有效地进行负采样仍未有效解决。为此,提出一种基于邻域采样的多任务图推荐算法。该算法提出了一种基于GNN的邻域采样策略,该策略以每个用户为中心构建子图,将次高阶物品作为用户邻域采样的负样本,可以更有效地挖掘强负样本并提高采样质量。通过GNN对图结点进行信息聚合与特征提取,得到结点的最终嵌入表示。设计一种余弦边际损失来过滤部分冗余负样本,以有效减少采样过程中的噪声数据。同时,该算法引入了多任务策略对模型进行联合优化,以增强模型的泛化能力。在3个公开数据集上进行的大量实验表明,该算法在大多数情况下明显优于其他主流算法。

In recent years,graph neural network(GNN)has become a mainstream method for collaborative filtering.It constructs user-item graphs to simulate interactions and utilizes GNN to learn their features.Although there have been sig�nificant advancements in model structures,effective negative sampling on graph structures remains challenging.To address this issue,a multi-task graph recommendation algorithm based on neighborhood sampling is proposed.Firstly,the algo�rithm introduces a neighborhood sampling strategy based on GNN,centering each subgraph around individual users and using higher-order items as negative samples for user neighborhood sampling.This approach effectively explores strong negative instances and enhances sampling quality.Secondly,GNN is employed to aggregate information and extract fea�tures from graph nodes,obtaining the final node embeddings.Finally,a margin loss of cosine is designed to filter redun�dant negative samples,effectively reducing noise in the sampling process.Additionally,the algorithm incorporates a multi�task strategy to jointly optimize the model,enhancing its generalization ability.Extensive experiments conducted on three public datasets demonstrate that this algorithm outperforms other mainstream methods in the majority of cases.