GCN Recommendation Algorithm Combining Negative Sampling and Message Passing
In recent years,Graph Convolutional Neural Networks(GCN)has been widely used in the recommendation field.LightGCN provides new ideas for the research of GCN by simplifying the tra-ditional GCN and omitting the process of feature transformation and nonlinear activation.In order to solve the negative sampling problem of recommendation algorithm and the influence of message passing on GCN convergence,SNGCN model is proposed,which changes the sampling strategy of sampling o-riginal negative samples directly from data and synthesizes hard negative samples using two steps of pos-itive example mixing and sample mixing.Secondly,SNGCN uses constrained loss to approximate the limit of multi-layer graph convolution.The final experimental results derived from this model on four publicly available benchmark datasets show that both its Recall and NDCG metrics are improved over the compared recommendation algorithms.
万方数据
维普
