基于图模型和注意力模型的会话推荐方法

为解决基于循环神经网络（RNN）会话推荐方法的兴趣偏好表示不全面、不准确问题，提出基于图模型和注意力模型的会话推荐（SR?GM?AM）方法。首先，图模型利用全局图和会话图分别获取邻域信息和会话信息，并且利用图神经网络（GNN）提取项目图特征，项目图特征经过全局项目表示层和会话项目表示层得到全局级嵌入和会话级嵌入，两种级别嵌入结合生成图嵌入；然后，注意力模型使用软注意力进行图嵌入和反向位置嵌入融合，目标注意力激活目标项目相关性，注意力模型通过线性转换生成会话嵌入；最后，SR?GM?AM经过预测层，输出下次点击的N项推荐列表。在两个真实的公共电子商务数据集Yoochoose和Diginetica上对比了SR?GM?AM方法与基于无损边缘保留聚合和快捷图注意力的推荐（LESSR）方法，结果显示，SR?GM?AM方法的P@20最高达到了72.41%，MRR@20最高达到了35.34%，验证了SR?GM?AM的有效性。

Session recommendation method based on graph model and attention model

To solve the problem that representation of interest preferences based on the Recurrent Neural Network （RNN） is incomplete and inaccurate in session recommendation， a Session Recommendation method based on Graph Model and Attention Model （SR?GM?AM） was proposed. Firstly， the graph model used global graph and session graph to obtain neighborhood information and session information respectively， and used Graph Neural Network （GNN） to extract item graph features， which were passed through the global item representation layer and session item representation layer to obtain the global? level embedding and the session?level embedding， and the two levels of embedding were combined into graph embedding. Then， attention model used soft attention to fuse graph embedding and reverse position embedding， target attention activated the relevance of the target items， as well as attention model generated session embedding through linear transformation. Finally， SR?GM?AM outputted the recommended list of the N items for the next click through the prediction layer. Comparative experiments of SR?GM?AM and Lossless Edge?order preserving aggregation and Shortcut graph attention for Session?based Recommendation （LESSR） were conducted on two real public e?commerce datasets Yoochoose and Diginetica， and the results showed that SR?GM?AM had the highest P@20 of 72.41% and MRR@20 of 35.34%， verifying the effectiveness of it.