基于会话的结合全局潜在信息的图神经网络推荐模型

Global potential information combined graph neural networks forsession-based recommendation

针对当前各种基于会话推荐的模型存在对物品间全局关系的获取和使用不足的问题,提出了基于会话的结合全局潜在信息的图神经网络推荐模型。该模型根据全部会话序列构建会话图与全局图,并在全局图中引入了序列中各节点间的间距信息,以及序列节点的相邻节点彼此之间的贡献度,通过模型训练获取最后的会话表征预测下一个交互行为。实验结果表明:在结合图神经网络的推荐算法中充分挖掘全局潜在信息可以有效提高推荐算法的准确率,这一改进对提高基于会话的图神经网络模型的性能有一定指导意义。

Aiming at the problem of insufficient acquisition and use of the global relationship between items in various current session based recommendation models,a session based graph neural network recommendation model combined with global potential information is proposed.The model constructs session graph and global graph according to all session sequences,introduces the spacing information between nodes in the sequence and the contribution of adjacent nodes of sequence nodes to each other,obtains the final session representation through model training,and predicts the next interaction behavior.The experimental results show that fully mining the global potential information in the recommendation algorithm combined with graph neural network can effectively improve the accuracy of the recommendation algorithm.This improvement has certain guiding significance for improving the performance of session based graph neural network model.