Sequential Recommendation Model Based on Smoothing Graph Masked Encoder
Aiming at the performance degradation problem of existing sequential recommendation models caused by label sparsity and user data noise,a sequential recommendation model based on smoothing graph masked encoder(SGMERec)is proposed.Firstly,a data smoothing encoder is designed to process the data,improve data quality and reduce the negative impact of extreme values and data noise.Secondly,a graph masked encoder is designed to adaptively extract transformation information from global items and a relational graph is constructed to help the model complete the missing label data,thereby enhancing the ability to deal with issues of label scarcity.Finally,batch normalization is employed to normalize the input distribution of each neural network layer.Thus,the stability of input distribution for each layer is guaranteed and the proportion of scarce labels in user sequences is reduced.Experimental results on three real datasets indicate the performance improvement of SGMERec.
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