二阶段孪生图卷积神经网络推荐算法

Two-stage recommendation algorithm of Siamese graph convolutional neural network

针对推荐系统中双塔型神经网络难以学习用户侧和商品侧交互信息以及图连接信息的问题,提出一种二阶段孪生卷积神经网络推荐算法(TSN)。首先,以用户行为构建异质图;然后,在双塔型神经网络之间设计图卷积孪生网络,从而在学习异质图连接信息的同时进行信息交互;最后,通过设计特殊结构的二阶段孪生信息共享机制,使得用户侧和商品侧的神经网络在训练过程中能够动态地、双向地传输信息,且有效避免神经网络串联。在基于MovieLens和豆瓣电影数据集的对比实验中,NDCG@10、NDCG@50、NDCG@100相较于最优基准算法DAT(Dual Augmented Two-tower model for online large-scale recommendation)提升了11.39%~23.98%。结果表明,所提算法能够缓解双塔型神经网络缺乏信息交互的问题,较对比算法推荐性能提升显著。

To solve the problem that the two-tower neural network in the recommendation system is difficult to learn the interaction information between the user side and the item side and the graph connection information,a new algorithm TSN(Two-stage Siamese graph convolutional Neural network recommendation algorithm)was proposed.First,a heterogeneous graph based on user behavior was built.Then,a graph convolutional Siamese network was designed between the two-tower neural networks,so as to achieve information interaction while learning the connection information of the heterogeneous graph.Finally,by designing a special structure of two-stage information sharing mechanism,the neural networks on the user side and the item side could transmit information dynamically and bidirectionally during the training process,and neural network cascading was effectively avoided.In comparative experiments on MovieLens and Douban movie datasets,the NDCG@10,NDCG@50,NDCG@100 of the proposed algorithm are 11.39%to 23.98%higher than those of the optimal benchmark algorithm DAT(Dual Augmented Two-tower model for online large-scale recommendation).The results show that the proposed algorithm can alleviate the problem of lack of information interaction in the two-tower neural network;and significantly improves the recommendation performance compared with the previous algorithms.