基于图注意力网络的多行为推荐算法

A Multi-Behavior Recommendation Algorithm Based on Graph Attention Network

现有的多行为推荐系统未有效利用不同层次的图传播信息,难以捕获用户不同行为的影响。为解决此问题,文章提出了一种基于图注意力网络的多行为推荐模型(GABR):首先,采用小批量采样节点嵌入方法聚合同一行为类型交互的邻域节点,以提高特征表示效率;接着,采用注意力机制学习不同行为类型的影响系数,以进一步融合节点特征;然后,合并多层用户和项目表示,以有效利用不同层次的图传播信息;最后,将已交互的用户-项目对和随机采样未交互过的用户-项目对作为正负样本对来训练目标模型,以优化模型性能。为验证模型推荐性能,在3个真实数据集(Yelp、Scholat、Beibei)上与现有9种推荐模型进行对比。实验结果表明GABR模型能够有效利用融合了不同行为类型影响系数的多层图传播信息,更好地预测用户偏好:在3个真实数据集上,与目前最佳的基线模型(GNMR)相比,GABR模型的HR、NDCG平均提高了1.73%、2.43%。

To address the issue that the existing multi-behavior recommendation systems do not effectively use di-fferent level graphs to spread information,and it is difficult to capture the impact strength of different actions on user decision making,a multi-behavior recommendation model based on graph attention network(GABR)is proposed.First,a mini-batch sampling mean aggregation method is used to aggregate neighboring nodes interacting with the same behavior type to improve the feature representation efficiency.Next,an attention mechanism is employed to learn the influence coefficients of different behavior types to further fuse node features.Then,multi-layer user and item representations are merged to effectively utilize graphs at different levels to propagate information.In the end,in order to optimize the model performance,the user-item pairs with interactions and randomly sampled user-item pairs without interactions are used as positive and negative sample pairs to train the target model.To verify the model recommendation performance,the GARB model is compared with 9 existing recommendation models on three real datasets(Yelp,Scholat,Beibei).The experimental results show that the GABR model can effectively use multi-layer graphs with influence coefficients of different behavior types to better predict user preferences.Compared with the best baseline model(GNMR)on three real datasets,the HR and NDCG of the GABR model increase by 1.73% and 2.43% on average.