基于平滑图掩码编码器的顺序推荐模型

Sequential Recommendation Model Based on Smoothing Graph Masked Encoder

针对现有顺序推荐模型在处理推荐任务时由于数据集标签稀缺和用户交互数据噪声导致性能降低的问题,提出基于平滑图掩码编码器的顺序推荐模型(Smoothing Graph Masked Encoder Recommender System,SGMERec).首先,设计数据平滑编码器处理数据,提升数据质量,降低极端值和数据噪声的负面影响.然后,设计图掩码编码器,自适应提取全局项目的转换信息,构造关系图帮助模型补全缺失的标签数据,提高模型对于标签稀缺问题的应对能力.最后,运用批标准化,归一化每个神经网络层的输入分布,确保每层输入的分布相对稳定,降低用户序列的稀缺标签比例.在3个真实数据集上的实验表明,SGMERec具有一定的性能提升.

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.