基于会话的多粒度图神经网络推荐模型

基于会话的推荐旨在根据当前用户的匿名会话的点击序列信息来预测用户的下一次点击行为。现有方法多数都是通过对用户会话点击序列的物品信息进行建模，并学习物品的向量表示，进而进行推荐。而作为一种粗粒度的信息，物品的类别信息对物品有聚合作用，可作为物品信息的重要补充。基于此，提出了基于会话的多粒度图神经网络推荐模型（SRMGNN）。首先，使用图神经网络（GNN）得到会话序列中的物品和物品类别的嵌入向量表示，并使用注意力网络捕捉用户的注意力信息；然后，将赋予了不同注意力权重值的物品和物品类别信息进行融合后，输入到门限循环单元（GRU）里；最后，通过GRU学习会话序列的物品时序信息，并给出推荐列表。在公开的Yoochoose数据集和Diginetica数据集上进行实验，实验结果验证了该模型在增加了物品类别信息后的优势，且实验结果表明了在Precision@20和MRR@20这2种评价指标上，该模型相较于短期注意力/记忆优先级（STAMP）模型、神经注意力（NARM）模型、GRU4REC等8种模型均有更好的效果。

Session-based recommendation model of multi-granular graph neural network

Session-based recommendation aims to predict the user’s next click behavior based on the click sequence information of the current user’s anonymous session. Most of the existing methods realize recommendations by modeling the item information of the user’s session click sequence and learning the vector representation of the items. As a kind of coarse-grained information， the item category information can aggregate the items and can be used as an important supplement to the item information. Based on this， a Session-based Recommendation model of Multi-granular Graph Neural Network （SRMGNN） was proposed. Firstly， the embedded vector representations of items and item categories in the session sequence were obtained by using the Graph Neural Network （GNN）， and the attention information of users was captured by using the attention network. Then， the items and item category information given by different weight values of attention were fused and input into the Gated Recurrent Unit （GRU）. Finally， through GRU， the item time sequence information of the session sequence was learned， and the recommendation list was given. Experiments performed on the public Yoochoose dataset and Diginetica dataset verify the advantages of the proposed model with the addition of item category information， and show that the model has better effect compared with all the eight models such as Short-Term Attention/Memory Priority （STAMP）， Neural Attentive session-based RecomMendation （NARM）， GRU4REC on the evaluation indices Precision@20 and Mean Reciprocal Rank （MRR）@20.