基于轻量图卷积和注意力增强的多行为推荐模型

近年来，图卷积网络被广泛应用于多行为推荐中，以进一步缓解数据稀疏问题。但目前许多方法都是直接使用图卷积网络，使得模型时间复杂度较高；还忽略了邻域的不同聚合权重和各行为对用户偏好的不同贡献。为此，提出一种基于轻量图卷积和注意力增强的多行为推荐模型（MB-LGCA）。首先根据多行为数据构建用户—项目二部图，采用一种轻量图卷积网络聚合邻域特征获得高阶协同信息，同时利用注意力机制融入邻域权重，增强节点嵌入表示；利用k-阶用户嵌入传播来获取各行为对用户偏好的不同重要性，使模型具有更好的可解释性；最后合并不同层的嵌入表示进行预测。两个真实数据集上的实验结果表明，该模型具有较好的性能。

Multi-behavior recommendation model based on light graph convolution and enhanced attention

In recent years, many researchers take advantage of graph convolution network in multi-behavior recommendation to further alleviate the data sparsity problem. However, most of current works directly use graph convolution network, which makes time complexity of the model relatively high. These works also ignore the different weights of neighbors and the different contributions of each behavior to user''s preference. Therefore, this paper proposed a multi-behavior recommendation model based on light graph convolution and enhanced attention(MB-LGCA). Firstly, the model constructed a user-item bipartite graph according to the multi-behavior data, and used a light graph convolution network to aggregate the features of neighbors to obtain high-order collaborative information. At the same time, it used attention mechanism to integrate the neighbors'' weights to enhance embedding representations of nodes. It used the k-order user''s embedding propagation to obtain the different importance of each behavior to user''s preference, so that the model had better interpretability. Finally, it combined embedding representations of different layers for prediction. The experimental results on two real datasets show that the model has better performance.