融合文本信息的轻量级图卷积网络推荐模型

在基于图卷积网络的推荐模型中，图卷积对仅包含编号信息的输入节点进行信息聚合会引发严重的瓶颈问题，影响推荐精度.为缓解此问题，考虑通过辅助信息丰富节点特征，提出了一种融合文本信息的轻量级图卷积网络推荐模型.模型把卷积神经网络提取出文本评论特征添加到图的节点嵌入中.为了简化图卷积网络结构，采用轻量级图卷积框架将融合的特征信息在用户-电影项目图上线性传播来学习用户和电影项目的嵌入，并将所有图卷积子层上特征嵌入的加权总和作为最终特征输出，用于预测评分.3个实际数据集上的实验结果表明，该方法可以缓解信息聚合瓶颈问题，提高推荐的准确度，并且模型可以缓解推荐中的冷启动问题.

A Lightweight Graph Convolutional Network Recommendation Model Incorporating Text Information

In the recommendation model based on graph convolution network,the graph convolution only aggregates information from the input nodes with identifier information, which will decrease the recommendation precision and, thus, lead to a bottleneck problem. To solve this problem,a lightweight graph convolution network recommendation model based on text information fusion is proposed by considering enriching node features with auxiliary information. The model extracts text comment features from convolution neural network and adds them to the node embedding of graph. To simplify the structure of graph convolution network,the proposed lightweight graph convolution framework is used to transmit the fused feature information linearly on the user-movie item graph to learn the embedding of the user and movie item. The weighted sum of all sub-levels of the graph convolution is used as the final feature output for predicting the rating. Experimental results on three real datasets show that the proposed method can alleviate the bottleneck problem of information aggregation and improve the accuracy of recommendation. The model can also alleviate the cold start problem.